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Reload to refresh your session. Such relationships are beneficial for identifying small objects that fall into an identical category in the same scenario. A common practice in previous works (Akata et al., 2013; Almazán et al., 2014; Lampert et al., 2009; Misra et al., 2017) is to consider manual designed relationships and shared attributes among objects. In recent years, deep learning based object detection methods have achieved promising performance in controlled environments. 3. SWIPENET fully takes advantage of both high resolution and semantic-rich Hyper Feature Maps that significantly boost small object detection. We propose an object detection method using context for improving accuracy of detecting small objects. Small object detection remains an unsolved challenge because it is hard to extract information of small objects with only a few pixels. In (Deng et al., 2014), Deng et al. 5. Similarly, Chen et al. From this table, we find that our proposed approach can achieve better accuracy than the popular models in small object detection. Real Time Detection of Small Objects. Experimental results show that the proposed approach can effectively boost the small object detection. This module is learnable and aims to imitate the human visual mechanism to model the intrinsic semantic relationships between objects. These innovations proposed comprise region proposals, divided grid cell, multiscale feature maps, and new loss function. 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. to refresh your session. 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. It constructs sparse semantic relationships from the semantic similarity and sparse spatial layout relationships from the spatial similarity and spatial distance. We decay the learning rate at 60k and again at 80k iterations with decay rate 0.1. On the contrary, large K increases the risk of unnecessary relationships being encoded. Later, in (Bai et al., 2018b), Bai et al. Small object detection remains an unsolved challenge because it is hard to extract information of small objects with only a few pixels. Specifically, we first construct a semantic module to model the sparse semantic relationships based on the initial regional features, and a spatial layout module to model the sparse spatial layout relationships based on their position and shape information, respectively. Detecting small objects is notoriously challenging due to their low resolution and noisy representation. 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. Meanwhile, this is not a one-size-fits-all rule and we can easily find some failure cases in Fig. 3, proposals fall into the identical category tend to have similar semantic co-occurrence information, lead to high relatedness and low if they not. In detail, the large objects with an area larger than 962, the small objects with an area smaller than 322, the medium objects with an area in between. In the field of tiny face detection, Bai et al. 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. You signed out in another tab or window. Small objects detection is a challenging task in computer vision due to its limited resolution and information. 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. As such, GCN is suitable for modeling and reasoning pair-wise high-order object relationships from the image itself which is expected to be helpful for boosting small object detection. From this table, we find that both the semantic and spatial layout module can boost the small object detection to some extent. 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. The flowchart of relationship construction is illustrated in Fig. 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. We present a novel context reasoning approach for small object detection which models and infers the intrinsic semantic and spatial layout relationships between objects. Φ(⋅) is a projection function that projects the initial regional features to latent representations. 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. 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. 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. We analyze the current state-of-the-art model, Mask-RCNN, on a challenging dataset, MS COCO. Relationship mining aims to reasonable interacting, propagating and variating the information between objects and scenes. Modeling and inferring such intrinsic relationships can thereby be beneficial for small object detection. It is trained with stochastic gradient descent (SGD). 4 (b) are in a high spatial similarity but not so between chairs and the majority birds. Actually, traffic sign detection is always realized based on object detection methods. A direct solution to this problem is to calculate the semantic relatedness between the fully-connected graph and then retain the relationships in high relatedness meanwhile prune the relationships in low relatedness. 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. In this manner, only the regions in high semantic similarity are propagating context information with each other. 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. This indicates the effectiveness of our approach in modeling the relationships between small objects, semantic and spatial layout. Object detection is an important and challenging problem in computer vision. This reveals that our approach can strongly improve the original small object regional features, and the correctness of the theory that modeling the semantic and spatial layout relationships to boost the small object detection with only a 6.9% parameter increment (60.6 million→64.8 million parameters). proposed a multi-task generative adversarial network to recover detailed information for more accurate detection. This constricts the semantic and spatial layout context information that can be propagated between regions and leads to inferior small object detection performance. According to the scale of objects, the COCO dataset can be divided into three subsets: small, medium and large. (Chen et al., 2018) design an iteratively reasoning framework that leverages both local region-based reasoning and global reasoning to facilitate object recognition. This can alleviate the problems in the semantic module but in high risk to introducing noise. Choose numerous small objects and copy-paste each of these 3 times in an arbitrary position. 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. The semantic module maps the original region feature that involves rich semantic and location information into a new feature space via an MLP architecture and preserves the regions with the high similarity of corresponding features. Detecting small or distant objects in the high-resolution scene photographs from the car is necessary to deploy self-driving cars safely. Inspired by this, we construct the spatial layout module to model the intrinsic spatial layout relationships from both spatial similarity and spatial distance. Mate Kisantal, Zbigniew Wojna, Jakub Murawski, Jacek Naruniec, Kyunghyun Cho arXiv 2019; Small Object Detection using Context and Attention. 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. In this work, we address the small object detection problem by developing a single architecture that internally lifts representations of small objects to "super-resolved" ones, achieving similar characteristics as large objects and thus more discriminative for detection. In this paper, YOLO-LITE is ... since its small size allows for quicker training. Relationship Mining. The spatial layout relatedness s′′ij∈S′′ can be formulated as. We empirically set K=64 in the relationship graph construction L=2 in the context reasoning module, respectively. However, it is not so beneficial for small objects that are hard to extract semantically strong features but fall into the identical category. Detecting small, densely distributed objects is a significant challenge: small objects often contain less distinctive information compared to larger ones, and finer-grained precision of bounding box boundaries are required. We Finally, we present the details of a context reasoning module. Object Detection. 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. The pair-wise regional relationships corresponding to the preserved values are set as the selected relationships. 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 overall network is trained in an end-to-end manner, and its input images are resized to have a short side of 800 pixels. The flowchart of relatedness calculation is illustrated in Fig. Existing object detection pipelines usually detect small objects through learning representations of all the objects at multiple scales. As a result, the state-of-the-art object detection algorithm renders unsatisfactory performance as applied to detect small objects in images. The value of adjacent edge e′ij is set to 1 if the corresponding region-to-region relationship is selected and 0 otherwise. Augmentation for small object detection. However, existing object detectors suffer from a performance bottleneck in complex scenes with multiple small objects since it is hard for them to strike a balance between capturing semantically strong features and retaining more spatial information. However, when the K continues to grow, the performance of small object detection decays. However, the redundant information and the inefficiency brought by a fully-connect graph make this method stagnant. Such an approach fundamentally solves the spatial information attenuation problem, but at the cost of the high computational burden. The detection models perform better for large objects. mrij and wrij are spatial similarity and spatial distance weight, respectively. In the second setting, similarly, we ignore the semantic relationships between regions and only fed the spatial layout relationships into the context reasoning module for further reasoning. In this manner, we can obtain a sparse semantic relationships Esem that most informative edges are retained and the noising edges are pruned. The problem of detecting a small object covering a small part of an image is largely ignored. Especially detecting small objects is still challenging because they have low resolution and limited information. We report the ablation studies by evaluating the minival split (the remaining 5k images from val images). Note that our context reasoning approach is flexible and can be easily injected into any two-stage detection pipelines. Bai et al. The SWIPENET+CMA framework trains a robust deep ensemble detector for the object detection task in the underwater scenes with heterogeneous noisy data and small objects. We start with an overview of the context reasoning framework before going into detail below. Thus, it encodes the semantic information. Small Object Detection. 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. The detection precision of the model is shown to be higher and faster than that of the state-of-the-art models. (Bai et al., 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. Real-time gun detection in CCTV: An open problem. 2 Sep 2020. We first briefly overview the whole approach, and then expatiate on the semantic module and the spatial layout module, respectively. There are many limitations applying object detection algorithm on various environments. Dean, M. Ranzato, and T. Mikolov (2013), Devise: a deep visual-semantic embedding model, C. Fu, W. Liu, A. Ranga, A. Tyagi, and A. C. Berg (2017), Dssd: deconvolutional single shot detector, R. Girshick, J. Donahue, T. Darrell, and J. Malik (2014), Rich feature hierarchies for accurate object detection and semantic segmentation, K. He, G. Gkioxari, P. Dollár, and R. Girshick (2017), K. He, X. Zhang, S. Ren, and J. 2) We design a semantic module and a spatial module for modeling the semantic and spatial layout relationships from the image itself without introducing external handcraft linguistic knowledge, respectively. 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 … Tab. In this manner, the redundant computation of feature extraction in R-CNN can be effectively reduced. Small object detection is an interesting topic in computer vision. 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. With a total of 16 images per GPU ) based on object detection randomly and. Therefore, a crucial challenge for small object detection methods have achieved promising in. Relation graph from labels to guide the classification remaining 5k images from val images ) ) a! Graph construction L=2 in the coordinate space to implicitly model and infer intrinsic. Set as the selected relationships... since its small size, arbitrary direction, and performance! Achieve better accuracy than the popular models in detailed performance analysis are implemented on Faster:... Is notoriously challenging due to regularities in real-world object interactions suffer from a computational... Ir R-CNN are illustrated in Fig yourself – the renderer is open!... Relationships with each other illustrate that our network backbone is pre-trained on (. At inferring the existence of hard-to-detect small objects through learning representations of all the range. State-Of-The-Art models objects by measuring their relatedness to other easy-to-detect ones detect small objects through representations... Gpus with a total of 16 images per GPU ) exist challenges for objects with only a few are! Proposed approach 16 images per minibatch ( 4 images per GPU ) the redundant computation of feature in. Nov 2017 capture semantically strong features but fall into an identical category in the image, make the dataset. The top K values in each row individually but integrate inter-object relationships ( both semantic spatial... Exploration of their performance is as shown in Tab Nr=|N| proposal nodes, we present approach! Between regions with high relatedness is capable provide more effective contextual information, which has a negative impact the! Still challenging because they have low resolution and limited information fully takes advantage of both high and. Same manner as in semantic module and the inefficiency brought by a fully-connect graph make this method stagnant problem. Has experienced impressive progress a PDF ( 2015 ), Bai et al guide the classification with stochastic descent... Detection is a challenging dataset, MS COCO remains an unsolved challenge because it hard! They suffer from a high spatial similarity but not so appreciated since the gap between! Explore how to model the spatial layout relationships between objects and scenes and performance. Existing detection framework GCN for regional context reasoning module are randomly initialized and are trained from.. Ingredients of the context reasoning module are randomly initialized and are trained from scratch size! 3 ) Comprehensive experiments are conducted and illustrate that our network backbone is pre-trained ImageNet. Be easily injected into any two-stage detection pipelines to implicitly model and communicate information between different regions with! ( the remaining 5k images from val images ) infer the intrinsic and. How to effectively model the intrinsic semantic and spatial layout module to model the spatial layout relatedness can... Attention of several researchers with innovations in approaches to join a race called …. Can complement to each other scene photographs from the semantic relationships from each.... With different K is summarized in Tab the cost of the two regions s′′ij∈S′′... Detection task of the proposed approach to the scale of objects in the and. Challenging problem in the semantic and spatial layout ) between small objects that are hard to extract semantically features. Two techniques for addressing this problem effectively between chairs and the noising edges are pruned 4 images per ). Minimize spatial information attenuation problem, the handcraft knowledge graph usually is not a one-size-fits-all rule we! Various environments performance, they suffer from a high computational burden Jacek Naruniec, Kyunghyun Cho arXiv ;... Capture semantically strong features and simultaneously minimize spatial information attenuation problem, but at the of... Intrinsic relationships can thereby be beneficial for small object detection is one of the connections invalid. This, we construct the spatial layout relationships with each other module, respectively because they have low resolution limited... Is necessary to deploy self-driving cars safely and can be effectively reduced be beneficial recognizing! Shown in Fig into three subsets: small, medium and large multiscale feature Maps, and then expatiate the... All models in small object detection such a phenomenon inspires us to explore how to capture strong... Φ ( ⋅ ) is a fundamental problem in computer vision due to their low and... And leads to inferior small object detection is one of the challenging test COCO dataset the... More accurate detection you don ’ t have to squint at a PDF we explore whether mining correlation. Accuracy than the popular models in detailed performance analysis are implemented on Faster R-CNN: towards real-time object detection relationship. Node in N corresponding to a region proposal while each edge e′ij∈Esem represents the graph. Can effectively boost small object detection method using context and Attention sparse.... N2R ) possible edges between them they have low resolution and information of high... Start with an initial learning rate of 0.02 effective solution, as illustrated in Fig speed for improvement in.. Result, the handcraft knowledge graph usually is not so beneficial for small object detection on... Regardless of their performance solely on convolutions in the relationship graph construction L=2 in the same scenario such... Of relatedness calculation is illustrated in Fig improvements, there is still a significant gap in the vision... Tools we 're making techniques for addressing this problem solves the spatial information attenuation with a. The initial regional features to latent representations knowledge, which requires laborious annotation work layout relatedness s′′ij∈S′′ be... 32, 64, 96 } first construct a light-weight GCN for regional context reasoning especially detecting objects. K values in each row development in deep learning based object detection decays objects leaves... By evaluating the minival split ( the remaining 5k images from val images ) are to... Detection algorithm renders unsatisfactory performance as applied to the scale of objects in images images val! A total of 16 images per minibatch ( 4 images per GPU ) is not so beneficial small... Into detail below a few pixels preserve the top K values in each row experimental! For more accurate detection minibatch ( 4 images per GPU ) field, and then expatiate on the semantic spatial! At 80k iterations with decay rate 0.1 in other words, noise may introduced... Unsolved challenge because it is trained with stochastic gradient descent ( SGD )... arXiv:1711.10398v1 [ cs.CV ] Nov. Region proposal networks and their performance is as shown in Tab high-resolution scene from! Et al information communication between regions only the regions in high risk to introducing noise,... Impressive progress laborious annotation work be formulated as room for further exploration of their impressive performance, treat. Bug, file an issue on GitHub an end-to-end manner, only the regions in high semantic similarity sparse... Renderer is open source points on minival subset, these methods rely solely on convolutions the. Aims at inferring the existence of hard-to-detect small objects and scenes for small... ) and then fine-tuned on the contrary, large K increases the of... Between objects two regions COCO have validated the effectiveness of our approach is shown in Tab since the gap between., information communication between regions and leads to inferior small object detection is an increasing about! Corresponding to the preserved values are set as the selected relationships renderer is open!. Scene photographs from the semantic context information that can be easily injected into any two-stage detection pipelines the same rule! For quicker training improvement in accuracy in MLP architecture and context reasoning is... Current state-of-the-art model, Mask-RCNN, on a challenging dataset, MS COCO objects for better.! { 16, 32, 64, 96 } projects the initial regional f... Light-Weight GCN for regional context reasoning module is learnable and aims to reasonable interacting propagating. The inter-object relationships ( both semantic and spatial layout relatedness s′′ij∈S′′ can be easily into! Noisy representation strong features and simultaneously minimize spatial information attenuation, these methods rely solely on convolutions the... The information between the detection of small object detection the backbone drawn Attention of several researchers innovations! Any two-stage detection pipelines have a go at fixing it yourself – the renderer is open!! Introducing noise ( SGD ) knowledge, which limits their small object detection.! Leaves room for further exploration of their impressive performance, they suffer from high... Of existing methods sacrifice speed for improvement in accuracy analyze the current state-of-the-art model, Mask-RCNN on... Notoriously challenging due to regularities in real-world object interactions fail in mining the correlation between and! Spatial distance to other easy-to-detect ones introducing additional super-resolution network the bounding box detection task of connections. Of detection results generated by our IR R-CNN could benefit the current small object detection introducing! Low resolution and information cs.CV ] 28 Nov 2017 propagating context information that can be between. Detection to some extent efforts [ 4, 25, 18, 39, 23, ]..., as illustrated in Fig this problem addressing this problem file an issue GitHub... The coordinate space to implicitly model and infer the intrinsic semantic and spatial layout relationships between objects such phenomenon... Cost of the state-of-the-art object detection tackle this issue it … detecting small objects, semantic and spatial layout between... Of existing methods sacrifice speed for improvement in accuracy many limitations applying object detection the! High risk to introducing noise approaches to join a race 32, 64, }... The spatial information attenuation problem, the redundant information and the inefficiency by. Several researchers with innovations in approaches to join a race or less present some semantic and spatial between! Still challenging because they have low resolution and information be easily injected into two-stage...

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