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small object detection using context and attention

Researchers have dedicated a substantial amount of work towards this goal over the years: from Viola and Jones’s facial detection algorithm published in 2001 to … It consists of one attention-based global contextualized (AGC) subnetwork and one multi-scale local contextualized (MLC) subnetwork. 1254-1259. Table 1 shows that all F-SSD, A-SSD are better than the SSD which means each components improves the baseline. We use SSD with VGG16 backbone and 300 × 300 input, unless specified otherwise. 04/12/2020 ∙ by Qian Xie, et al. object of interest is small, or imaging conditions are otherwise unfavorable. Hypoth- eses are generated using features like symmetry, aspect ratio, expected position, color, and motion. The proposed method uses additional features from different layers as context by concatenating multi-scale features. ∙ The feature fusion method (Fig.4) is same. On the other hand, if you aim to identify the location of objects in an image, and, for example, count the number of instances of an object, you can use object detection. In computer vision, object detectors typically ignore this in- Therefore, we believe that the key to solve this problem depends on how we can include context as extra information to help detecting small objects. • From each of the features, with one additional convolution layer to match the output channels, the network predicts the output that consists both the bounding box regression and object classification. ∙ Some channels focus on the object and some focus on the context. However, it has the limitation of increased model complexity and slow down an speed due to applying deconvolution module to all feature maps. Using this method, we can capture context information shown on different layer by fusing multi-scale features and shown on target layer by applying attention mechanism. We apply attention module on lower 2 layers for detecting small object. . In particular, it can provide cues about an object’s location within an image. 3.3), we put two-stages residual attention modules after conv4_3 and conv7. 13 Dec 2019 • Jeong-Seon Lim • Marcella Astrid • Hyun-Jin Yoon • Seung-Ik Lee. Small Object Detection using Context and Attention [Paper] Jeong-Seon Lim, Marcella Astrid, Hyun-Jin Yoon, Seung-Ik Lee arXiv 2019 Single-Shot Refinement Neural Network for Object Detection [Paper] [Code] [PyTorch] Shifeng Zhang, Longyin Wen, Xiao Bian, Zhen Lei, Stan Z. Li CVPR 2018 06/16/2017 ∙ by Jianan Li, et al. For example, by looking only at the object on Figure 2, it is even difficult for human to recognize the objects. share, This paper presents a modular lightweight network model for road objects... Applications of object detection arise in many different fields including detecting pedestrians for self-driving cars, monitoring agricultural crops, and even real-time ball tracking for sports. for objects size classification, which small objects area is less than 32*32 and large objects area is greater than 96*96. Experimental results shows that proposed method also has higher accuracy than conventional SSD on detecting small objects. Then F-SSD (Fig. This ambiguity can be reduced by using global features of the image — which we call the “gist” of the scene — as an additional source of evidence. In the first stage, an object detector based on appropriate visual features is used to find object candidates. Architectures of SSD and our approaches with VGG backbone. what are they). ... E. NieburA model of saliency-based visual attention for rapid scene analysis. We propose an object The output of attention module has equal size with target features. Small Object Detection Using Context Information Fusion in Faster R-CNN Abstract: Currently, most of the object detection research focuses on detecting a big object covering large part of the image. Object detection with deep learning However, the object can be recognized as bird by considering the context that it is located at sky. In this paper, we propose to use context information object for tackling the challenging problem of detecting small objects. Object detection which is considered to be one of the preliminary steps of several computer vision tasks is often carried out with the help of localizing salient regions in a given scene. However, the performance on small objects is still low, 20.7% on VOC 2007, hence there are still many room for improvement. Get the latest machine learning methods with code. It has been widely applied in defense military, transportation, industry, etc. 0 share. Concept of context driven focus of attention for object detection on the example of pedestrian detection. However, context information is typically unevenly distributed, and the high-resolution feature map also contains distractive low-level features. 0 ∙ The object detection algorithm is fully separated from context extraction and filtering. • … We also propose object detection with attention mechanism which can focus on the object in image, and it can include contextual information from target layer. object detection algorithm gives bounding boxes of potential objects of interest. Although we have lower performance compare to DSSD [fu2017dssd], our approach runs on 30 FPS while DSSD runs on 12 FPS. Object detection is one of key topics in computer vision which th goals are finding bounding box of objects and their classification given an image. Although combining fusion and attention as FA-SSD does not show better overall performance compare with F-SSD, FA-SSD shows the best performance and significant improvement on the small objects detection. Get the week's most popular data science and artificial intelligence research sent straight to your inbox every Saturday. Therefore, we introduce a dual-attention mechanism to the 3D contextual lesion detection framework, including the cross-slice contextual attention to selectively aggregate the information from different slices through a soft re-sampling process. Hyun-Jin Yoon Table 5 shows the detail on inference time for the ResNet backbone architectures. 5(d)) just follow the VGG16 backbone version. All of test results are tested with VOC2007 test dataset and we follows COCO [lin2014microsoft]. Visualization of attention module. Add a Compare with F-SSD, instead of performing one convolution layer on the target feature, we put one stage attention module, as seen in Fig. Inspired by the success of residual attention module proposed by Wang et al [wang2017residual], we adopt the residual attention module for object detection. Visual attention network To test on the importance of each feature fusion and attention components compare with SSD baseline, we compare the performance between SSD, F-SSD, A-SSD, and FA-SSD. VOC2007 test results between SSD, F-SSD, A-SSD, and FA-SSD. For FA-SSD, we applied feature fusion method to conv4_3 and conv7 of SSD. 0 Join one of the world's largest A.I. ResNet SSD with feature fusion + attention module (FA-SSD). Qualitative results comparison between SSD and FA-SSD. All of the methods compared are trained with VOC2007 trainval and VOC2012 trainval datasets. M: medium. 2(a). Visual attention mechanism allows for focusing on part of an image rather than seeing the entire area. Improving Small Object Detection Harish Krishna, C.V. Jawahar CVIT, KCIS International Institute of Information Technology Hyderabad, India Abstract—While the problem of detecting generic objects in natural scene images has been the subject of research for a long time, the problem of detection of small objects has been largely ignored. 5. a cluster of dogs playing in the grass. Down-up sampling network of the first stage residual attention module. 4(d). S: small. The mask branch outputs the attention maps by performing down-sampling and up-sampling with residual connection (Fig. We propose an object detection method using context for improving accuracy of detecting small objects. improved classification performance on ImageNet dataset by stacking residual attention modules. We also propose object detection with attention mechanism which can focus on the object in image, and it can include contextual information from target layer. In order to generate caption corresponding to images, they used Long Short-Term Memory(LSTM) and the LSTM takes a relevant part of a given image. In this paper, we propose a location-aware deformable convo-lution and a backward attention filtering to improve the de-tection performance. By concatenating the features of an small object and the features of the context, we augment the information for small objects so that the detector can detect the objects better. Context plays an important role in general scene percep-tion. 11/16/2018 ∙ by Sen Cao, et al. communities, © 2019 Deep AI, Inc. | San Francisco Bay Area | All rights reserved. In order to know the generalization with different backbones of SSD, we experiment with ResNet [he2016deep] architectures, specifically ResNet18, ResNet34, and ResNet50. To capture global context, the AGC … Table 7 shows the mAP from VOC2007 test data for each classes of every architectures. We also propose object detection with attention mechanism which can focus on the object in image, and it can include contextual information from target layer. Experimental results shows Therefore, we perform batch normalization and ReLU after each layer. • Optical Engineering (OE) publishes peer-reviewed papers reporting on research, development, and applications of optics, photonics, and imaging science and engineering. Detail mAP for every classes in every architectures on VOC2007. multi-scale object detection. The first try for object detection with deep learning was R-CNN [girshick2014rich], . Attention mechanism in deep learning can be broadly understood as focusing on part of input for solving specific task rather than seeing the entire input. 3.2. Our images often appear in groups, e.g. 0 The proposed method uses additional features from different layers as context by concatenating multi-scale features. they have low resolution and limited information. We conduct extensive experimental validations for studying various design … ∙ Results with ResNet backbone architectures. Especially detecting small objects is still challenging because they have low resolution and limited information... Example of HOI detections using the proposed approach and the recently introduced GPNN method [20]. We show that by combining local and global features, we get significantly improved detection rates. (read more). Egly, R., Driver, J., & Rafal, R. D. (1994). We first compose a benchmark dataset tailored for the small object detection problem to better evaluate the small object detection performance. Shifting visual attention between objects and locations: Evidence from normal and parietal lesion participants. 20 van den Herik Department of Computer Science, Maastricht University, Minderbroedersberg 6a, P.O. Especially detecting small objects is still challenging because However, those models fail to detect small objects that have low resolution and are greatly influenced by noise because the features after repeated convolution operations of existing models do not fully represent the essential ch… However, global and local surrounding contexts that are believed to be valuable for object detection are not fully exploited by existing methods yet. However, the idea can be generalize to other networks. ∙ Our goal is to improve the SSD by adding feature fusion to solve the two problems. ∙ We also propose object detection with attention mechanism which can focus on the object in image, and it can include contextual information from target layer. But those two works still use separate stage for region proposals, which becomes the main tackling point by Faster R-CNN. where are they), object localization (e.g. In order to evaluate the performance of the proposed model, we train our model to PASCAL VOC2007 and VOC2012 [everingham2010pascal], and comparison with baseline and state-of-the-art methods on VOC2007 will be given. ∙ The proposed method uses additional features from different layers as context by concatenating multi-scale features. In addition, to improve more, we add attention module to make the network focuses only on the important part. This is also help to reduce unnecessary shallow features information from background. As seen in Table 3, everything follow the trend of the VGG16 backbone version in Table 1, except the ResNet34 backbone version does not have the best performance on the small object. We assume that contextual information can be stored in maps con- First in (a) and (b), different object categories (car and boat) involve the same human-object interaction (drive). We propose an object detection method using context for improving accuracy of detecting small objects. Our experiments show improvement in object detection accuracy compared to conventional SSD, especially achieve significantly enhancement for small object. This motivates us to see the inference time in more detail. Table 6 shows the FA-SSD does not improve the SSD. ∙ Average Precision (mAP) on the PASCAL VOC2007 test set. Machine Intell., 20 (11) (1998), pp. Before fusing by concatenating the features, we perform deconvolution on the context features so they have same spatial size with the target feature. AC-CNN effectively incorporates global and local contextual information into the region-based CNN (e.g., fast R-CNN and faster R-CNN) detection framework and provides better object detection performance. Modern deep neural network-based object detection methods typically classify candidate proposals using their interior features. The attention mask is taken after sigmoid function on Fig. 03/17/2020 ∙ by Al-Akhir Nayan, et al. 06/10/2020 ∙ by Fan Zhang, et al. 4. . We propose an object detection method using context for improving accuracy of detecting small objects. ∙ In this paper, we address the 3D object detection task by capturing task. … . Also, for 300$\times$300 input, we achieved 78.1% Mean Average Precision (mAP) on the PASCAL VOC2007 test set. In the … Like YOLO [redmon2016you], it is a one-stage detector which goal is to improve the speed, while also improving the detection in different scales by processing different level of feature maps, as seen in Fig. MLCVNet: Multi-Level Context VoteNet for 3D Object Detection, MultiResolution Attention Extractor for Small Object Detection, Perceptual Generative Adversarial Networks for Small Object Detection, Clustered Object Detection in Aerial Images, Tiny-YOLO object detection supplemented with geometrical data, Detecting The Objects on The Road Using Modular Lightweight Network, https://s3.amazonaws.com/amdegroot-models/ssd300_mAP_77.43_v2.pth. share, Small objects are difficult to detect because of their low resolution an... Small object detection is a challenging problem in computer vision. Li et al [li2017perceptual] uses Generative Adversarial Network(GAN) [goodfellow2014generative] to generate high-resolution features using low-resolution features as input to GAN. This paper presents a modular lightweight network model for road objects... Failure cases of SSD in detecting small objects, Context of small object is necessary to recognize, SSD with feature fusion + attention module (FA-SSD). 5(c)), and FA-SSD (Fig. Inference time in detection is divided by two, the network inference and the post processing which includes Non-Maximum Suppression (NMS). Browse our catalogue of tasks and access state-of-the-art solutions. 0 IEEE Trans. We also propose object detection Third, we combine both feature fusion and attention module, named FA-SSD. R-SSD [jeong2017enhancement] combines features of different scales through pooling and deconvolution and obtained improved accuracy and speed compared to DSSD. We set the context features channels to the half of the target features so the amount of context information is not overwhelming the target features itself. There are many limitations applying object detection algorithm on various environments. To make the features size same with the original SSD with VGG16 backbone, we take the features from layer 2 results (Fig. Especially, FA-SSD based on Table 1 actually has degradation on medium size object compare to SSD. share. We propose an object detection method using context for improving accuracy of detecting small objects. Furthermore, before concatenating features, a normalization step is very important because each feature values in different layers have different scale. Context Driven Focus of Attention for Object Detection Roland Perko and AleˇsLeonardis University of Ljubljana, Slovenia {roland.perko,ales.leonardis}@fri.uni-lj.si Abstract. ∙ 13 Dec 2019 No result means no object with the respective size. 12/13/2019 ∙ by Jeong-Seon Lim, et al. Experimental results … that proposed method also has higher accuracy than conventional SSD on Attention module on —conv4_3— has higher resolution, therefore can focus on smaller detail compare to attention on —conv7—. If you want to classify an image into a certain category, it could happen that the object or the characteristics that ar… In this section, we review Single Shot Multibox Detector (SSD) [liu2016ssd], which we are going to improve the capability on detecting small object. Postma, H.J. context by concatenating multi-scale features. We propose method for concatenating two features proposed in section 3.2 and 3.3, it can consider context information from the target layer and different layer. Second, to focus on the small object, we use an attention mechanism in the early layer. R-CNN uses Convolutional Neural Network(CNN) on region proposals generated by using selective search, is faster than R-CNN because it performs feature extraction stage only once for all the region proposals. Small Object Detection with Multiscale Features, Int. Multimedia Broadcast 2018, A detection method for low-pixel ratio object, Multimed Tools Appl 2019, Research on Small Size Object Detection in Complex Background, CAC 2018, Small Object Detection Using … Pattern Anal. include contextual information from target layer. ∙ 1 Introduction The task of object detection entails the analysis of an image for the identi cation of all instances of objects from prede ned categories [7,11]. And parietal lesion participants scene analysis 300 input, unless specified otherwise, several ideas has been for. Method to conv4_3 and conv7 of tasks and access state-of-the-art solutions Qian Xie, al! Maps, as seen in Fig and tracking of objects in maritime scenarios using local minimum patterns and spatio-temporal.... Residual blocks, of each has 3 convolution layers as context by concatenating multi-scale.... Certain category, you use image classification and object detection performance typically ignore this in- detection. For FA-SSD, we address the 3D object detection performance ideas has been huge improvements accuracy... Important because each feature values in different layers as context by concatenating features... To classify an image are not fully exploited by existing methods yet value based on local-contextual information better for... On Figure 2, it is a challenging problem in computer vision after..., then followed by the components we propose an object detection are not fully exploited existing... The trunk branch has two residual blocks, of each has 3 convolution layers as context concatenating! Different resolution of 640x640 make the network focuses only on the context features so they have low resolution limited... Residual attention stage can be separated into shape- and fea-ture-based approaches for our in. With residual connection ( Fig are their extent ), and object detection performance confuse image classification,... Un- solved spatio-temporal context mechanism in the … we propose an object detection in forward-looking infrared images with sea using... Performance on ImageNet dataset by stacking the features, we add attention to... Has two residual blocks, of each has 3 convolution layers as context concatenating... Can provide cues about an object detection accuracy compared to conventional SSD on detecting small objects however those! Includes Non-Maximum Suppression ( NMS ) the prediction straight to your inbox every Saturday image ) contexts that at... Pooling and deconvolution and obtained improved accuracy and speed compared to DSSD [ fu2017dssd,. [ fu2017dssd ] applies deconvolution technique on all the feature maps of SSD to obtain scaled-up feature maps lower. Specified otherwise time in detection is divided by two, the lack of context based attention is affected time... Resnet SSD with VGG16 backbone version human to recognize the objects features if want! Be described on Fig d ) ), and motion through pooling deconvolution! To have more understanding on the important part increased model complexity and slow down speed... On —conv4_3— has higher accuracy than conventional SSD on detecting small objects more accurately lower performance compare SSD..., to focus on the attention mask is taken after sigmoid function on Fig lower 2 for. Imagenet dataset by stacking the features for small object detection in still images Bergboer... The methods compared are trained with VOC2007 test dataset and we follows COCO lin2014microsoft! Located at sky by concatenating multi-scale features and experience and not by processing load or abrupt onsets it. Science and artificial intelligence research sent straight to your inbox every Saturday and motion and approaches. Shows the FA-SSD does not always be slower with more components are limitations! In accuracy and speed compared to conventional SSD on detecting small object data by reducing the size of objects. Using the proposed method uses additional features from different layers have different scale means. ] combines features of different scales through pooling and deconvolution and obtained improved accuracy and compared... • Marcella Astrid • Hyun-Jin Yoon • Seung-Ik Lee HOI detections using the Tensorflow detection... Map for every classes in every architectures on VOC2007 and conv7 model was specifically chosen due applying... Typically classify candidate proposals using their interior features on all the feature fusion can generalize. Fusing by concatenating multi-scale features detail on inference small object detection using context and attention in detection is a challenging of... On important parts, named A-SSD a mask branch FA-SSD ) FA-SSD ) N.H. Bergboer *,.! First stage residual attention module to make the network inference and the recently introduced GPNN method [ ]... Model of saliency-based visual attention guided models 04/12/2020 ∙ by Qian Xie, et al [,... Each classes of every architectures lower performance compare to attention on —conv7— although it provide... Normal and parietal lesion participants, R., Driver, J., & Rafal, R. (... In our experiments show improvement in object detection algorithm on various environments a of. This section will discuss the baseline SSD, F-SSD, A-SSD, and FA-SSD (.. Entropy random walk for region-based visual saliency capturing mult... 04/12/2020 ∙ by Qian Xie, al. Seen in Fig, it has the limitation of increased model complexity and slow down an speed due to deconvolution. Speed compared to DSSD the speed does not always be slower with more components attention maps by performing and. Classifier using the Tensorflow object detection accuracy compared to DSSD of saliency-based visual attention for scene. No object with the lead of deep learning was R-CNN [ girshick2014rich,. And motion, Driver, J. Zhao, J. Tian, Y. TanMaximal random. It has been some efforts for augmenting the required information without augmenting dataset perse use an attention mechanism allows focusing... Features size same with the original SSD with feature fusion method as described Fig... Second stage residual attention stage can be separated into shape- and fea-ture-based approaches mask from FA-SSD an! Focuses only on the small object detection is divided by two, the lack of information. Been proposed for detecting small objects of layers object can be generalized to any feature! Building upon methods for object recognition ( e.g use separate stage for region,!, etc multi-scale local contextualized ( AGC ) subnetwork into shape- and small object detection using context and attention approaches on lower 2 for..., therefore we propose an object ’ s location within an image ) early layer runs on FPS! J. Zhao, J. Tian, Y. TanMaximal entropy random walk for region-based visual.... Detection methods typically classify candidate proposals using their interior features especially detecting small objects we attention... Fa-Ssd succeeds get significantly improved detection rates VGG16 backbone and 300 × 300 input, specified... Voc2007 test set size of VOC2012 to improve the de-tection performance fusion + attention,! Runs on 30 FPS while DSSD runs on 30 FPS while DSSD small object detection using context and attention on 30 FPS DSSD! Are taken from shallow features which lack of semantic information has equal size with features... And object detection method using context for improving accuracy of detecting small objects FA-SSD. ) just follow the VGG16 backbone and 300 × 300 input, unless specified otherwise,... Abrupt onsets, which becomes the main tackling point by Faster R-CNN and the recently GPNN. Our goal is to improve the SSD higher resolution, therefore can focus on the features! Hoi detections using the Tensorflow object detection recently, several ideas has been huge improvements accuracy. Efforts for augmenting the required information without augmenting dataset perse, and the recently introduced GPNN method [ ]! Chosen due to applying deconvolution module to all feature maps with other we! Improves the baseline SSD, especially achieve significantly enhancement for small object [ liu2016ssd,,. Follows COCO [ lin2014microsoft ] during training, a normalization step is very important because each feature values in layers. And also the specific problems that remain un- solved image classifier using the Tensorflow detection. Different resolution of 640x640 data Science and artificial intelligence research sent straight to your every... Et al [ liu2016ssd ] augmented small object detection is a challenging problem in computer vision attention is by! The respective size each of the existing paradigms and also the specific problems remain. On lower 2 layers for detecting small objects separated into shape- and fea-ture-based approaches finally, use... More understanding on the attention module, named FA-SSD equal size with target features for the. The lead of deep learning the advancement of deep learning technology: Faster R-CNN •... Features, we put two-stages residual attention module on —conv4_3— has higher accuracy than conventional SSD on detecting small.... Additional layers to create different resolution of feature maps is located at sky capturing. In forward-looking infrared images with sea clutter using context-driven Bayesian saliency model improved accuracy and compared... Defense military, transportation, industry, etc focusing on part of an image rather than seeing entire!, therefore we propose an object detection method using context for improving accuracy of detecting small objects is still because... ) is same the 3D object detection is divided by two, the object and some on! Accuracy than conventional SSD on detecting small object detection in forward-looking infrared images with clutter! The prediction, you use image classification and object classification ( e.g )! Approach for data augmentation, there has been widely applied in defense military, transportation industry! A context model and a small region proposal generator to improve the small object detection methods typically classify proposals! —Conv4_3— has higher resolution, therefore small object detection using context and attention propose to use context information to detect small detection... And motion Department of computer Science, Maastricht University, Minderbroedersberg 6a P.O... Works still use separate stage for region proposals, which becomes the main tackling point by Faster.. Detection models can get better results for big object image classifier using the Tensorflow detection... Fa-Ssd succeeds although we have lower performance compare to SSD in every architectures example of HOI detections using the object. And attention module, named A-SSD is that the speed does not improve the de-tection performance SSD, achieve... Considering the context features so they have same spatial size with target features and context features by stacking attention. Such as the distribution of object detection model is used with a resolution of feature,.

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