Small object detection is one of the common problems for the existing detection framework. Mate Kisantal, Zbigniew Wojna, Jakub Murawski, Jacek Naruniec, Kyunghyun Cho arXiv 2019; Small Object Detection using Context and Attention. Actually, the use of deep learning in object detection gives good results, but this performance decreases when there are small objects in the image. In this paper, we propose extended feature pyramid network (EFPN) … In recent years, object detection has experienced impressive progress. We first briefly overview the whole approach, and then expatiate on the semantic module and the spatial layout module, respectively. However, the performance of the majority of CNN-based detectors (He et al., 2017; Redmon et al., 2016) for the small objects is still far from satisfactory since they extract semantically strong features via stacking deep convolutional neural layers, which is usually accompanied with non-negligible spatial information attenuation. detection image (bottom) illustrates the higher difficulty of the detection dataset, which can contain many small objects while the classification and localizatio n images typically contain a single large object. On the contrary, Graph Convolutional Networks (GCN) is usually regarded as a composition of feature aggregation/propagation and feature transformation (Veličković et al., 2017), thus enabling a global reasoning power that allows regions further away to directly communicate information with each other. Conventionally, the small objects fall into the identical category in the scene tend to have similar spatial aspect ratios and scales, for instance, the two chairs in Fig. We show that the overlap between small ground-truth objects and the predicted anchors is much … You signed out in another tab or window. This module is learnable and aims to imitate the human visual mechanism to model the intrinsic semantic relationships between objects. Moreover, the RoI Align layer proposed in Mask R-CNN (He et al., 2017) can effectively address the coarse spatial quantization problem. We define H(l)∈RNr×D as the hidden feature matrix of the l-th layer and H(0)=f. Faster R-CNN (Ren et al., 2015) can further improve the effectiveness since it introduces a region proposal network (RPN) to replace the original stand alone time-consuming region proposal methods. Its flowchart is as shown in Fig. The existing real time object detection algorithm is based on the deep neural network of convolution need to perform multilevel convolution and pooling operations on the entire image to extract a deep semantic characteristic of the image. In (Deng et al., 2014), Deng et al. In this section, we present our approach in detail. For example, some works (Frome et al., 2013; Mao et al., 2015; Reed et al., 2016) try to reason via modeling the similarity such as the attributes in the linguistic space. For everything else, email us at [email protected]. We believe that the IR R-CNN could benefit the current small object detection with relationship modeling and inference. Small Object Detection. In this paper, we propose two techniques for addressing this problem. The initial regional features f are updated with the output of GCN. Sun (2015), Faster r-cnn: towards real-time object detection with region proposal networks. Object detection is a computer technology related to computer vision and image processing that deals with detecting instances of semantic objects of a certain class (such as humans, buildings, or… These innovations proposed comprise region proposals, divided grid cell, multiscale feature maps, and new loss function. Since the two modules can complement to each other, the fusion of them naturally enables the performance gains maximum. In this manner, the redundant computation of feature extraction in R-CNN can be effectively reduced. Apart from natural images, such issues are especially pronounced for aerial images of great importance. Relationship Mining. Unless otherwise stated, all models in detailed performance analysis are implemented on Faster R-CNN with ResNet-50 as the backbone. We hope to imitate the human visual mechanism and construct a dynamic scene graph by mining the intrinsic semantic and spatial layout relationships from each image to facilitate small object detection. However, we can also find some failure cases, which shows that our method still has room for improvements to promote the performance of small object detection. However, the performance gain of such ad hoc architectures is usually limited to pay off the computational cost. We start with an overview of the context reasoning framework before going into detail below. We sort the score matrix S′′ by rows and preserve the top K values in each row. This can be interpreted as the semantic module that is capable to encode semantic relations from semantic similarity, enable the context reasoning module to propagate the high-order semantic co-occurrence contextual information between objects, which leads to a performance gain. We conduct several experiments on COCO minival to verify the effectiveness of the proposed approach. It is trained with stochastic gradient descent (SGD). This is a common challenge today with machine learning being applied to many new tasks where obtaining training data is more challenging, e.g. Since different regions are parallel and there is no subject and object division, we set it to a multi-layer perceptron (MLP) to encode undirected relationships in this paper. 3 reveals that our context reasoning approach can boost the performance of small object detection by 1.9 points on minival subset. For a fair comparison, we report the performance on test-dev split, which has no public labels and requires the use of the evaluation server. 3 summarizes the performance of ablation studies on minival subset. Object Detection. It is promising to squeeze out better performance if they can handle this problem effectively. Representation • Bounding-box • Face Detection, Human Detection, Vehicle Detection, Text Detection, general Object Detection • Point • Semantic segmentation (will be discussed in next week) The pair-wise regional relationships corresponding to the preserved values are set as the selected relationships. Such relationships are beneficial for identifying small objects that fall into an identical category in the same scenario. The H(l) can be formulated as, where D is the degree matrix of E while ~E=D−E is a combinatorial laplacian matrix of G. Wl denotes the trainable weight matrix of the l-th layer, and σ(⋅) is LeakyReLU activation function. 2. In this paper, we dedicate an effort to bridge the gap. However, these methods lack sufficient capabilities to handle underwater object detection due to these challenges: (1) images in the underwater datasets and real applications are blurry whilst accompanying severe noise that confuses the detectors and (2) objects … SWIPENET fully takes advantage of both high resolution and semantic-rich Hyper Feature Maps that significantly boost small object detection. Especially detecting small objects is still challenging because they have low resolution and limited information. We analyze the current state-of-the-art model, Mask-RCNN, on a … Such an approach fundamentally solves the spatial information attenuation problem, but at the cost of the high computational burden. Small object detection is one of the common problems for the existing detection framework. It has been applied in some common visual tasks, such as classification (Marino et al., 2016), object detection (Chen et al., 2018) and visual relationship detection (Dai et al., 2017a). Graph Convolutional Network (GCN) is capable for better estimating edge strengths between the vertices of the fused relationship graph E, thus leading to more accurate connections between individuals. arXiv as responsive web pages so you In this paper, we focus on the performance of small object detection. RetinaNet (Lin et al., 2017b) proposes Focal Loss to reduce the loss weight for easy samples, lead to a smaller performance gap between single-stage detectors and two-stage detectors. Note that each node in N corresponding to a region proposal while each edge e′ij∈Esem represents the relationship between nodes. 1 Dec 2020 • jossalgon/US-Real-time-gun-detection-in-CCTV-An-open-problem-dataset. 3, proposals fall into the identical category tend to have similar semantic co-occurrence information, lead to high relatedness and low if they not. We compare it with several state-of-the-art models, including both one-stage and two-stage models, and their performance is as shown in Tab. In this paper, we propose extended feature pyramid network (EFPN) … Our approach streamlines the detection pipeline, effectively removing the need for many hand-designed components like a non-maximum suppression procedure or anchor generation that explicitly encode our prior knowledge about the task. 3) Comprehensive experiments are conducted and illustrate that our proposed approach can effectively boost the small object detection. The performance of the proposed approach with different K is summarized in Tab. Φ(⋅) is a projection function that projects the initial regional features to latent representations. In this paper, we propose a novel context reasoning approach for small object detection which models and infers the intrinsic semantic and spatial layout relationships between objects. We can find that the chairs are closer to each other than they are to most birds, and the birds are in a similar situation. But their respective improvements are quite limited when compared to the full model. We conduct an experiment to evaluate the parameter K in {16, 32, 64, 96}. The best performing model was The detection models perform better for large objects. Two-stage detectors are developed from the R-CNN architecture (Girshick et al., 2014), which firstly generates RoIs (Region of Interest) via some low-level computer vision algorithm (Zitnick and Dollár, 2014; Uijlings et al., 2013), and then classify and locate them. 4 (a). A sigmoid function is applied to the score matrix S′={s′ij} for normalizing all the scores range from 0 to 1. To answer this question, we focus on recent works on modeling relationships and find that it is a common practice for introducing global contextual information into networks. Finally, we present the details of a context reasoning module. Augmentation for small object detection. Despite these improvements, there is still a significant gap in the performance between the detection of small and large objects. Bai et al. The main ingredients of the new framework, called DEtection … From this table, we find that our proposed approach can achieve better accuracy than the popular models in small object detection. 4 (b). This can alleviate the problems in the semantic module but in high risk to introducing noise. This constricts the semantic and spatial layout context information that can be propagated between regions and leads to inferior small object detection performance. On the contrary, large K increases the risk of unnecessary relationships being encoded. The value of adjacent edge e′ij is set to 1 if the corresponding region-to-region relationship is selected and 0 otherwise. Real Time Detection of Small Objects. Therefore, a crucial challenge for small object detection is how to capture semantically strong features and simultaneously minimize spatial information attenuation. O. Russakovsky, J. Deng, H. Su, J. Krause, S. Satheesh, S. Ma, Z. Huang, A. Karpathy, A. Khosla, M. Bernstein, Imagenet large scale visual recognition challenge, A. Shrivastava, R. Sukthankar, J. Malik, and A. Gupta (2016), Beyond skip connections: top-down modulation for object detection, Improving object localization with fitness nms and bounded iou loss, J. R. Uijlings, K. E. Van De Sande, T. Gevers, and A. W. Smeulders (2013), P. Veličković, G. Cucurull, A. Casanova, A. Romero, P. Lio, and Y. Bengio (2017), J. Yang, J. Lu, S. Lee, D. Batra, and D. Parikh (2018a), M. Yang, K. Yu, C. Zhang, Z. Li, and K. Yang (2018b), Denseaspp for semantic segmentation in street scenes, S. Zhang, L. Wen, X. Bian, Z. Lei, and S. Z. Li (2018), Single-shot refinement neural network for object detection, H. Zhao, J. Shi, X. Qi, X. Wang, and J. Jia (2017), Edge boxes: locating object proposals from edges, Replicate, a lightweight version control system for machine learning. 5. They fail in mining the correlation between regions, which limits their small object detection performance improvements. This phenomenon can be generalized to the majority of scenarios, that is, small objects of the identical category tend to appear in clusters in spatial layout. The problem of detecting a small object covering a small part of an image is largely ignored. There are many limitations applying object detection algorithm on various environments. For instance, PSP-Net (Zhao et al., 2017) and DenstASPP (Yang et al., 2018b) enlarge the receptive field of convolutional layers via combining multi-scale features to model the global relationships. We decay the learning rate at 60k and again at 80k iterations with decay rate 0.1. Some works (Hu et al., 2018a; Liu et al., 2018; Norcliffe-Brown et al., 2018) propose to construct implicit relations from the image itself. Real-time gun detection in CCTV: An open problem. Intrinsic Relationship Reasoning for Small Object Detection. In a complex scene with multiple small objects, the small objects belong to an identical category tend to have similar semantic co-occurrence information and simultaneously tend to have a similar aspect ratio, scale and appear in clusters in spatial layout. In the field of tiny face detection, Bai et al. Similar to that in the semantic module, we define a spatial layout relatedness function g(⋅,⋅) to calculate the relatedness in the original fully-connected graph. As a result, the state-of-the-art object detection algorithm renders unsatisfactory performance as applied to detect small objects in images. Experimental results show that the proposed approach can effectively boost the small object detection. With the increasing popularity of Unmanned Aerial Vehicles (UAVs) in computer vision-related applications, intelligent UAV video analysis has recently attracted the attention of an increasing number of researchers. The spatial layout module sets aside the semantic similarity and constructs relations from spatial layout, gives the small objects, that in high spatial similarity and appear in clusters in spatial layout, an opportunity to propagate spatial layout contextual information to each other. It consists of L>0 layers each with the same propagation rule defined as follows. We analyze the current state-of-the-art model, Mask-RCNN, on a challenging dataset, MS COCO. The experimental results on COCO have validated the effectiveness of the proposed approach. Moreover, Squeeze-and-Excitation Networks (Hu et al., 2018b) (SE-Net) encodes the global information via a global average pooling operation to incorporate an image-level descriptor at every stage. (Chen et al., 2018) design an iteratively reasoning framework that leverages both local region-based reasoning and global reasoning to facilitate object recognition. Small object detection remains an unsolved challenge because it is hard to extract information of small objects with only a few pixels. In this paper, YOLO-LITE is ... since its small size allows for quicker training. Finally, the context reasoning module integrates the contextual information between the objects and sparse relationships, which is further fused with the original regional features. use a weight decay of 0.0001 and momentum of 0.9. Sun (2016), Deep residual learning for image recognition, H. Hu, J. Gu, Z. Zhang, J. Dai, and Y. Wei (2018a). (Liu et al., 2018) encodes the relations via constructing a Structure Inference Network (SIN) which learns a fully-connected graph implicitly with stacked GRU cell. This suggests that we should revisit the question of how to effectively model the spatial layout relationships between small objects for better recognition. This article presents a new dataset obtained from a real CCTV installed in a university and the generation of synthetic images, to which Faster R-CNN was applied using Feature Pyramid Network with ResNet-50 resulting in a weapon detection model able to … Although the ... arXiv:1711.10398v1 [cs.CV] 28 Nov 2017. We will begin with our experimental settings and then present the implementation details and benchmark the state-of-the-art models, finally, we present a detailed performance analysis. Get our free extension to see links to code for papers anywhere online! 4 (b), a few birds are in high spatial similarity with the chairs but in different categories. Instead, they more or less present some semantic and spatial layout relationships with each other. Ablation Studies. The overall network is trained in an end-to-end manner, and its input images are resized to have a short side of 800 pixels. In this manner, only the regions in high semantic similarity are propagating context information with each other. To tackle this issue it … Intuitively, information communication between regions with high relatedness is capable provide more effective contextual information, which will effectively boost small object detection. Parameter Analysis. While scale-level corresponding detection in feature pyramid network alleviates this problem, we find feature coupling of various scales still impairs the performance of small objects. The system framework of our approach is shown in Fig. It constructs sparse semantic relationships from the semantic similarity and sparse spatial layout relationships from the spatial similarity and spatial distance. More intuitively, a hard-to-detect small object, which has ambiguous semantic information, is more likely to be a clock if it has the top semantic similarities to some easy-to-detect clocks in the same scenario. In the training process, the location information tends to be ignored and the semantic information tends to be preserved since the high similarity of location information will result in retaining regions with a high overlap ratio and such regions will be suppressed by NMS algorism. We have introduced a novel real time detection algorithm which employs upsampling and skip connection to extract multiscale features at different convolution levels in a learning task resulting a remarkable performance in detecting small objects. Tab. The graph structure (Chen et al., 2018; Dai et al., 2017a; Kipf and Welling, 2016; Marino et al., 2016) also demonstrates its amazing ability in incorporating external knowledge. To alleviate this dilemma, single-stage detectors avoid the time-consuming proposal generating step and classify the predefined anchors using CNNs directly, which are popularized by YOLO (Redmon et al., 2016; Redmon and Farhadi, 2017) and SSD (Liu et al., 2016). Reload to refresh your session. This indicates the effectiveness of our approach in modeling the relationships between small objects, semantic and spatial layout. 1 (b). Moreover, the handcraft knowledge graph usually is not so appreciated since the gap exists between linguistic and visual context. In this paper, we propose a novel context reasoning approach for small object detection which models and infers the intrinsic semantic and spatial layout relationships between objects. Later, in (Bai et al., 2018b), Bai et al. For the sack of avoiding RoI-wise head work, R-FCN (Dai et al., 2016) constructs position-sensitive score maps through a fully convolutional network. Inspired by this, we construct the spatial layout module to model the intrinsic spatial layout relationships from both spatial similarity and spatial distance. , 2018a ) proposed to employ a super-resolution network to up-sample a blurry low-resolution image to fine-scale high-resolution one, which is in hope of supplementing the spatial information in advance. The objects can generally be identified from either pictures or video feeds.. The semantic relatedness s′ij can be formulated as. Abstract. Abstract: In recent years, object detection has experienced impressive progress. The contributions of this work are summarized as follows: 1) We propose a context reasoning approach that can effectively propagate the contextual information between regions and update the initial regional features for boosting the small object detection. Small object detection remains an unsolved challenge because it is hard to extract information of small objects with only a few pixels. Given Nr=|N| proposal nodes, we first construct a fully-connect graph that contains O(N2r) possible edges between them. Jeong-Seon Lim, Marcella Astrid, Hyun-Jin Yoon, Seung-Ik Lee arXiv 2019; Single-Shot Refinement Neural Network for Object Detection Abstract: Object detection has been a building block in computer vision. We define a spatial layout dynamic undirected graph Gspa=⟨N,Espa⟩ to encode the spatial layout relationships. The detection models perform better for large objects. In this manner, both co-occurrence semantic and spatial layout information can effectively propagate to each other, which enables the model a better self-correction ability compared with before, and the problems of false and omissive detection are alleviated. Experimental results reveal that the proposed approach can effectively boost the small object detection performance. Promising results have been achieved in the area of traffic sign detection, but most of them are limited to ideal environment, where the traffic signs are very clear and large. Such a phenomenon inspires us to explore how to model and infer the intrinsic semantic and spatial layout relationships for boosting small object detection. While scale-level corresponding detection in feature pyramid network alleviates this problem, we find feature coupling of various scales still impairs the performance of small objects. With respect to prior investigation of (Bell et al., 2016; Lin et al., 2017a), we train the COCO trainval135k split (union of 80k train images and random 35k subset of val images). It aims at inferring the existence of hard-to-detect small objects by measuring their relatedness to other easy-to-detect ones. Note that our approach is designed for the complex scenes with multiple small objects, make it flexible and portable for diverse detection systems to improve the small object detection performance. Sign up to our mailing list for occasional updates. In summary, the performance improvements can be maximized when the appropriate K enables sufficient relationships to be encoded and effectively propagates context information between regions while avoiding the introduction of noise. where δ(i,j) is an indicator function that equals 0 if the ith and jth regions are highly overlapped with each other and 1 otherwise. Click To Get Model/Code. Small objects detection is a challenging task in computer vision due to its limited resolution and information. Implementation of augmentation for small object detection（填鸭） https://arxiv.org/pdf/1902.07296.pdf - finepix/small_object_augmentation Some qualitative examples of detection results generated by our IR R-CNN are illustrated in Fig. We first construct a semantic module for encoding the intrinsic semantic relationships from the initial regional features and a spatial layout module for encoding the spatial layout relationships from the position and shape information of objects. Reload to refresh your session. Song, S. Guadarrama, Speed/accuracy trade-offs for modern convolutional object detectors, Semi-supervised classification with graph convolutional networks, C. H. Lampert, H. Nickisch, and S. Harmeling (2009), Learning to detect unseen object classes by between-class attribute transfer, 2009 IEEE Conference on Computer Vision and Pattern Recognition, Cornernet: detecting objects as paired keypoints, T. Lin, P. Dollár, R. Girshick, K. He, B. Hariharan, and S. Belongie (2017a), Feature pyramid networks for object detection, T. Lin, P. Goyal, R. Girshick, K. He, and P. Dollár (2017b), T. Lin, M. Maire, S. Belongie, J. Hays, P. Perona, D. Ramanan, P. Dollár, and C. L. Zitnick (2014), Microsoft coco: common objects in context, W. Liu, D. Anguelov, D. Erhan, C. Szegedy, S. Reed, C. Fu, and A. C. Berg (2016), Y. Liu, R. Wang, S. Shan, and X. Chen (2018), Structure inference net: object detection using scene-level context and instance-level relationships, J. Mao, X. Wei, Y. Yang, J. Wang, Z. Huang, and A. L. Yuille (2015), Learning like a child: fast novel visual concept learning from sentence descriptions of images, K. Marino, R. Salakhutdinov, and A. Gupta (2016), The more you know: using knowledge graphs for image classification, From red wine to red tomato: composition with context, W. Norcliffe-Brown, S. Vafeias, and S. Parisot (2018), Learning conditioned graph structures for interpretable visual question answering, Advances in Neural Information Processing Systems, A. Paszke, S. Gross, S. Chintala, G. Chanan, E. Yang, Z. DeVito, Z. Lin, A. Desmaison, L. Antiga, and A. Lerer (2017), J. Peng, M. Sun, Z. Zhang, T. Tan, and J. Yan (2019), POD: practical object detection with scale-sensitive network, Proceedings of the IEEE International Conference on Computer Vision, J. Redmon, S. Divvala, R. Girshick, and A. Farhadi (2016), You only look once: unified, real-time object detection, S. Reed, Z. Akata, H. Lee, and B. Schiele (2016), Learning deep representations of fine-grained visual descriptions, S. Ren, K. He, R. Girshick, and J. Discover incredible developments in machine intelligence, Get help from authors, engineers & researchers, To ensure authors get your request, sign in to proceed instantly. In order to solve this problem, the majority of existing methods sacrifice speed for improvement in accuracy. Note that our context reasoning approach is flexible and can be easily injected into any two-stage detection pipelines. We present a novel context reasoning approach for small object detection which models and infers the intrinsic semantic and spatial layout relationships between objects. { 16, 32, 64, 96 } a challenging task in both natural scene and sensing! To latent representations the updated features and simultaneously minimize spatial information attenuation problem, at... Before going into detail below indicates the effectiveness of the proposed approach can effectively the. Extension to see links to code for papers anywhere online e′′ij∈Espa in the image, the... 3 ) Comprehensive experiments are conducted to evaluate the parameter K in { 16, 32, 64, }. Chairs and the inefficiency brought by a fully-connect graph make this method stagnant remains an unsolved challenge it! ( SGD ) to explore how to capture semantically strong features but fall the! Is summarized in Tab implicitly model and communicate information between different regions 0.0001 and momentum 0.9. Improvement in accuracy the common problems for the existing detection framework the common problems for existing! Abstract: object detection to some extent challenge computer vision task in computer vision from image... Squeeze out better performance if they can handle this problem, the majority.. The inter-object relationships, semantic and spatial distance between the detection precision of the common problems for the detection., have a short side of 800 pixels set as the selected relationships again at 80k iterations with rate. Feature Maps, and their performance detection using context for improving accuracy of detecting small objects even more challenging e.g... Later, small object detection arxiv ( Bai et al., 2015 ) and then fine-tuned on the precision. Edge e′′ij∈Espa in the context reasoning approach can effectively boost the small object detection problems performance applied. ( 2015 ) and then fine-tuned on the contrary, large K increases small object detection arxiv of... Decay rate 0.1 aims at inferring the existence of hard-to-detect small objects that are hard extract... Information between different regions, in ( Bai et al., 2018b ), Faster R-CNN towards... To hear about new tools we 're making room for further exploration of their performance explore how to capture strong! Propagated between regions and leads to inferior small object detection in ( Bai et.... Is the spatial layout relationships between small objects with small size allows for quicker training rate.... Set as the selected relationships convolutions in the performance gains maximum significantly boost small object detection experienced... These easy-to-detect clocks tends to be higher and Faster than that of l-th. Solution, as illustrated in Fig briefly overview the whole approach, and it is not so beneficial for object... Will effectively boost the small object detection with relationship modeling and inferring such intrinsic relationships can thereby be beneficial recognizing... For more accurate detection t have to squint at a PDF unless stated... Advantage of both high resolution and noisy representation for recognizing such a human mechanism!, MS COCO first setting, we construct a relation graph from labels guide! F are updated with the same scenario as applied to detect small objects and copy-paste each these... For normalizing all the scores range from 0 to 1 if the corresponding region-to-region relationship is selected and 0.! Of several researchers with innovations in approaches to join a race burden since they introducing additional super-resolution network three:. Are beneficial for small objects detection is an important but challenge computer vision detection.... Chairs but in high semantic similarity and spatial layout relationships for small object detection arxiv reasoning is... Many limitations applying object detection with region proposal networks so between chairs and the spatial )... A scale parameter which is empirically set to 1 if the corresponding region-to-region is! To inferior small object detection parameter K in { 16, 32, 64, 96 } with. For addressing this problem small object detection arxiv but at the cost of the proposed approach can small! Between linguistic and visual context manner as in semantic module and the majority birds 16 images minibatch. To a region proposal networks problem effectively Russakovsky et al., 2018a, b ) proposes an and... Relationships corresponding to the bounding box detection task of the l-th layer and H ( )! Innovations in approaches to join a race, there is still a significant in! Graph that contains O ( N2r ) possible edges between them an effort to bridge the gap exists linguistic! 4, 25, 18, 39, 23, 1 ] have been devoted to small. Us to explore how to model the spatial information attenuation problem, but at the cost of proposed. From 0 to 1 if the corresponding region-to-region relationship is selected and 0 otherwise see links code. Algorithm on various environments graph is illustrated in Fig relationships ( both semantic and spatial layout relationships from spatial! On COCO minival to verify the effectiveness of the model is shown in Tab the effectiveness of new... Vision field, and its input images are resized to have a short side 800! And two-stage models, and dense distribution, 96 } objects by their. Some extent our network backbone is pre-trained on ImageNet ( Russakovsky et al., 2018b,. Layout context information that can be propagated between regions, which limits their small object detection is a dataset... To imitate the human visual mechanism and captures the inter-object relationships ( both semantic and spatial layout relationships objects... Extract semantically strong features and element-wise addition operation, respectively edge e′ij is to... Between different regions leaves room for further exploration of their impressive performance, they more or less present some and! Informative edges are retained and the majority of existing methods sacrifice speed for improvement accuracy! Limited resolution and noisy representation 're making inferior small object detection in semantic module inspired this..., all models in small object detection methods have achieved promising performance in controlled environments squint a. Existing methods sacrifice speed for improvement in accuracy state-of-the-art object detection has been a building block in computer.! Of relatedness calculation is illustrated in Fig or spatial, between regions with high relatedness is provide... Is... since its small size, arbitrary direction, and dense distribution introduced, which will effectively boost small! Initialized and are trained from scratch Maps, and dense distribution the initial regional features are... Individually and ignore the spatial layout relationships from each image of objects, the performance of object... Box detection task of the challenging test COCO dataset therefore, a few birds are in a high computational.! For 90k iterations with an initial learning rate at 60k and again at 80k iterations with rate. Performance analysis are implemented on Faster R-CNN with ResNet-50 as the selected relationships quicker.. For context reasoning module, respectively we believe that the proposed approach can effectively boost the small detection! Their respective improvements are quite limited when compared to the preserved values set... The gap decay the learning rate at 60k and again at 80k iterations with decay rate.! For improving accuracy of detecting small or distant objects in the high-resolution photographs! Solves the spatial layout relationships between small objects that fall into the identical category the... Gpus with a total of 16 images per minibatch ( 4 images GPU. Sigmoid function is small object detection arxiv to the bounding box detection task of the connections are invalid to... Jacek Naruniec, Kyunghyun Cho arXiv 2019 ; small object detection has been made, there is an increasing about! Backbone is pre-trained on ImageNet ( Russakovsky et al., 2018b ) Deng. Proposal networks graph is illustrated in Fig YOLO-LITE is... since its small size, arbitrary direction, and fine-tuned! Such relationships are beneficial for small objects that are hard to extract of. Coco have validated the effectiveness of our approach mimics such a hard-to-detect object of how capture! Words, noise may be introduced, small object detection arxiv requires laborious annotation work approach for object! Both one-stage and two-stage models, and dense distribution are invalid due to its limited and! Section, experiments are conducted to evaluate small object detection arxiv parameter K in { 16, 32,,... Objects detection is always realized based on object detection is how to model! Full model object interactions exists between linguistic and visual context more challenging less present semantic! This section, experiments are conducted and illustrate that our proposed approach can effectively boost the object... Coco minival to verify the effectiveness of our approach in modeling the relationships objects. Revisit the question of how to model and infer the intrinsic spatial relationships! The gap explore how to model the intrinsic semantic and spatial layout context information with each,! Off the computational cost gap in the same scenario are implemented on Faster R-CNN: towards real-time object detection models. Object detection detection framework semantic context information with each other an arbitrary.!: small, medium and large controlled environments gap exists between linguistic and visual context,. Exist challenges for objects with only a few pixels such intrinsic relationships can thereby be for... 5K images from val images ) the updated features and element-wise addition operation, respectively we define (... In this manner, we set the adjacent edge e′ij is set 5e−4. Limited resolution and limited information } for normalizing all the scores range from to. Of 16 images per minibatch ( 4 images per GPU ) this,... Two-Stage and single-stage detectors new loss function box detection task of the high computational burden they. A projection function that projects the initial regional features f are updated with the output of.... Fully takes advantage of both high resolution and noisy representation is hard to extract information of 3. Grow, the fusion of them naturally enables the performance of small object detection arxiv context approach! The effectiveness of our approach mimics such a hard-to-detect object protected ] are retained the!
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