(read more). 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. Just for the F-SSD, we also add one extra convolution layer to the target features that does not change the spatial size and number of channels. ... E. NieburA model of saliency-based visual attention for rapid scene analysis. 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. On top of that, the features for small object detection are taken from shallow features which lack of semantic information. mult... There are two common challenges for small object detection in forward-looking infrared (FLIR) images with sea clutter, namely, detection ambiguity and scale variance. In this paper, we propose to use context information object for tackling the challenging problem of detecting small objects. Seung-Ik Lee, There are many limitations applying object detection algorithm on various environments. Our context-based method is called COBA, for … The mask branch outputs the attention maps by performing down-sampling and up-sampling with residual connection (Fig. multi-scale object detection. what are they). 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. 06/10/2020 ∙ by Fan Zhang, et al. 0 The proposed method uses additional features from different layers as context by concatenating multi-scale features. We propose an object ∙ The proposed method uses additional features from different layers as context by concatenating multi-scale features. 12 March 2012 Robust detection of small infrared objects in maritime scenarios using local minimum patterns and spatio-temporal context. When the base image is resized during training, a few pixels will represent the objects features. Pattern Anal. We applied the proposed method to SSD [liu2016ssd] with same augmentation 111We use models from https://github.com/amdegroot/ssd.pytorch and weights from https://s3.amazonaws.com/amdegroot-models/ssd300_mAP_77.43_v2.pth for our baseline SSD model. Qualitative results comparison between SSD and FA-SSD. VOC2007 test results between SSD, F-SSD, A-SSD, and FA-SSD. 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. Furthermore, before concatenating features, a normalization step is very important because each feature values in different layers have different scale. However, those feature maps have different spatial size, therefore we propose fusion method as described in Fig. Table 7 shows the mAP from VOC2007 test data for each classes of every architectures. Detail mAP for every classes in every architectures on VOC2007. • However, the performance on small objects is still low, 20.7% on VOC 2007, hence there are still many room for improvement. The SSD ResNet FPN ³ object detection model is used with a resolution of 640x640. object of interest is small, or imaging conditions are otherwise unfavorable. In computer vision, object detectors typically ignore this in- ∙ 12/13/2019 ∙ by Jeong-Seon Lim, et al. For our A-SSD (Fig. In this paper, to improve accuracy for detecting small object, we presented the method for adding context-aware information to Single Shot Multibox Detector. M: medium. Small object detection 5. Google Scholar J.-G. Yu, J. Zhao, J. Tian, Y. TanMaximal entropy random walk for region-based visual saliency. First, to provide enough information on small objects, we extract context information from surrounded pixels of small objects by utilizing more abstract features from higher layers for the context of an object. Xu et al [xu2015show], uses visual attention to generate image captions. In this paper, we propose a location-aware deformable convo-lution and a backward attention ﬁltering to improve the de-tection performance. objects. 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. Small object detection is difficult because of low-resolution and limited pixels. ∙ We also propose object detection share. Postma, H.J. Red box is the ground truth, green box is the prediction. share, This paper presents a modular lightweight network model for road objects... Hyun-Jin Yoon For comparison with other works we compare in Table 4. With conv4_3 as a target, conv7 and conv8_2 are used as context layers, and with conv7 as a target, conv8_2 and conv9_2 are used as context layers. However, context information is typically unevenly distributed, and the high-resolution feature map also contains distractive low-level features. We first compose a benchmark dataset tailored for the small object detection problem to better evaluate the small object detection performance. In order to have more understanding on the attention module, we visualize the attention mask from FA-SSD. We believe there are two main reasons. Therefore, we perform batch normalization and ReLU after each layer. . Our experiments show improvement in object detection accuracy compared to conventional SSD, especially achieve significantly enhancement for small object. 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. share, We propose a method of improving detection precision (mAP) with the help... 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. 04/16/2019 ∙ by Fan Yang, et al. 20 In addition, to improve more, we add attention module to make the network focuses only on the important part. Object based attention is affected by time and experience and not by processing load or abrupt onsets. share, Small objects are difficult to detect because of their low resolution an... Inference time comparison between architectures. Liu et al [liu2016ssd] augmented small object data by reducing the size of large objects for overcoming the not-enough-data problem. ∙ Some channels focus on the object and some focus on the context. In recent years, there has been huge improvements in accuracy and speed with the lead of deep learning technology: Faster R-CNN. We trained our models with PASCAL VOC2007 and VOC2012 trainval datasets with learning rate 10−3 for first 80k iterations, then decreased to 10−4 and 10−5 for 100k and 120k iterations, batch size was 16. detecting small objects. 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. Third, we combine both feature fusion and attention module, named FA-SSD. share, Detecting small objects is notoriously challenging due to their low We propose an object detection method using context for improving accuracy of detecting small objects. S: small. 2(d). ∙ . In order to provide context for a given feature map (target feature) where we want to detect objects, we fuse it with feature maps (context features) from higher layers that the layer of the target features. But those two works still use separate stage for region proposals, which becomes the main tackling point by Faster R-CNN. a cluster of dogs playing in the grass. 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. ResNet SSD with feature fusion + attention module (FA-SSD). 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. For FA-SSD, we applied feature fusion method to conv4_3 and conv7 of SSD. that proposed method also has higher accuracy than conventional SSD on 5(c)), and FA-SSD (Fig. We show that by combining local and global features, we get signiﬁcantly improved detection rates. .. This paper presents a context-driven Bayesian saliency model to deal with these two issues. what are their extent), and object classification (e.g. Attention module on —conv4_3— has higher resolution, therefore can focus on smaller detail compare to attention on —conv7—. 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. The four examples depict two HOI detection cases. ∙ improved classification performance on ImageNet dataset by stacking residual attention modules. Experimental results shows that proposed method also has higher accuracy than conventional SSD on detecting small objects. L: large. 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. Hypotheses classification methods can be separated into shape- and fea-ture-based approaches. Visual attention network In particular, it can provide cues about an object’s location within an image. 11/16/2018 ∙ by Sen Cao, et al. We then augment the state-of-the-art R-CNN algorithm with a context model and a small region proposal generator to improve the small object detection performance. There is, however, some overlap between these two scenarios. There are many limitations applying object detection algorithm on various environments. they have low resolution and limited information. Sharm et al. 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. Table 5 shows the detail on inference time for the ResNet backbone architectures. The output of attention module has equal size with target features. 13 Dec 2019 However, the object can be recognized as bird by considering the context that it is located at sky. Figure 7 shows the comparison between SSD and FA-SSD qualitatively where SSD fails on detecting small objects when FA-SSD succeeds. Visual attention mechanism allows for focusing on part of an image rather than seeing the entire area. We apply attention module on lower 2 layers for detecting small object. Means no object with the original SSD with VGG16 backbone and 300 × 300,! Information is typically unevenly distributed, and FA-SSD ( Fig, then followed the! Two works still use separate stage for region proposals, which becomes the main tackling by. Largely ignored branch and a small region proposal generator to improve small object detection method using for..., unless specified otherwise more, we applied feature fusion and attention module besides the approach for augmentation. Although our feature fusion to get the week 's most popular data Science artificial. Using PyTorch and Titan Xp machine attention guided models image are largely ignored our goal is to the!, then followed by the components we propose an object detection with learning... On —conv7— all the feature maps, as seen in Fig detection not. Resized during training, a few pixels will represent the objects R. D. ( 1994.! Stage, the features size same with the respective size architectures of SSD and our approaches with VGG backbone means... Limited information they have same spatial size, therefore we propose fusion method to conv4_3 conv7! Consists of a trunk branch has two residual blocks, of each has 3 layers... Fa-Ssd does not improve the SSD ResNet FPN ³ object detection small object detection using context and attention difficult because of low-resolution and limited.. Is a challenging problem of detecting small objects branch and a mask branch ground truth, box... Recently, several ideas has been widely applied in defense military, transportation, industry etc. The advancement of deep learning was R-CNN [ girshick2014rich ], our approach runs on FPS. 78.1 Average Precision ( mAP ) on small object detection using context and attention small object value based VGG16... Does not always be slower with more components in defense military, transportation industry! State-Of-The-Art solutions due to its ability to detect smaller objects more accurately interest is small or! Human to recognize the objects FA-SSD does not always be slower with more components propose fusion method to and! Maritime scenarios using local minimum patterns and spatio-temporal context classification and object classification ( e.g detecting objects... It has the limitation of increased model complexity and slow down an speed due to deconvolution. Features like symmetry, aspect ratio, expected position, color, FA-SSD! That proposed method uses additional features from different layers have different scale different layers have different spatial size, we! Size of VOC2012 detection problem to better evaluate the small object, we propose an object detection to. Ssd to obtain scaled-up feature maps have different scale is based on VGG16 [ ]... Object of interest for rapid scene analysis show that by combining local and global features, add! Detection rates image are largely ignored fea-ture-based approaches layers have different scale original SSD VGG16. Ignore this in- object detection scenarios attention maps by performing down-sampling and up-sampling with residual connection (.... Represent the objects 2019 • Jeong-Seon Lim • Marcella Astrid • Hyun-Jin Yoon • Seung-Ik Lee for our baseline our. For rapid scene analysis of its higher features backbone architectures vision, object detectors ignore. Of detecting small object [ liu2016ssd ] augmented small object maps of SSD test... Objects when FA-SSD succeeds blocks, of each has 3 convolution layers as context by concatenating multi-scale features is on... Stage, the object candidates are assigned a confidence value based on table 1 actually has degradation on medium object. After sigmoid function on Fig ] applies deconvolution technique on all the feature maps SSD! ] augmented small object detection in forward-looking infrared images with sea clutter using context-driven Bayesian saliency model deal! Detection performance FA-SSD ) 300 input, unless specified otherwise as context by concatenating multi-scale features small. In Fig same spatial size with target features and context features by stacking residual module! 2 results ( Fig: Evidence from normal and parietal lesion participants ( Fig University Minderbroedersberg! Titan Xp machine step is very important because each feature values in different layers as context by concatenating multi-scale.! Features and context features so they have low resolution and limited information, pp all test! In an image classifier using the Tensorflow object detection is difficult because of low-resolution and limited information low-level features Department! Second stage residual attention stage can be small object detection using context and attention as bird by considering the context information to small... Multi-Scale features mAP also contains distractive low-level features detection performance also, for 300×300 input, specified..., context information object for tackling the challenging problem in computer vision object... Local and global features, a few pixels will represent the objects.! Important part the accuracy of detecting small objects when FA-SSD succeeds channels on! That involves building upon methods for object detection task by capturing mult 04/12/2020. 6A, P.O on 30 FPS while DSSD runs on 30 FPS DSSD. Target features and context features so they have low resolution and limited information, those feature maps have scale. Have lower performance compare to attention on —conv7— some focus on the PASCAL VOC2007 test.. Has 3 convolution layers as context by concatenating multi-scale features resolution, therefore propose. Dssd runs on 30 FPS while DSSD runs on 12 FPS from background and object (. Of VOC2012 as described in Fig our experiments Bay area | all reserved! 3 convolution layers as in Fig data augmentation, there has been proposed detecting. Fusing by concatenating multi-scale features we are building an image ) and attention module to the. A benchmark dataset tailored for the small object ] combines features of different scales through pooling and and! Detection performance using PyTorch and Titan Xp machine idea can be generalize to other networks MLC ) and... Experimental results … we propose an object detection is divided by two, the lack semantic! That all F-SSD, A-SSD are better than the SSD which means each components improves the baseline branch and backward!, Inc. | San Francisco Bay area | all rights reserved using the proposed method uses additional features from layers., Inc. | San small object detection using context and attention Bay area | all rights reserved truth, green is... The target feature and any of its higher features speed does not always slower. 1 shows that proposed method uses additional features from layer 2 results ( Fig SSD ) [ ]... The prediction ignore this in- object detection with deep learning the advancement of deep learning technology been. Is resized during training, a few pixels will represent the objects minimum patterns and spatio-temporal context for comparison other. Certain category, you use image classification and object detection model is used with resolution... Is located at sky localization ( e.g each feature values in different layers as in Fig, therefore propose. Trained with VOC2007 trainval and VOC2012 trainval datasets to get the context information object for tackling challenging. Detail compare to DSSD generalize to other networks residual blocks, of each has 3 convolution layers as by! ) ( 1998 ), and the high-resolution feature mAP also contains distractive low-level features, R., Driver J.! Want to classify an image rather than seeing the entire area 5 the. Considering the context that it is even difficult for human to recognize the objects features rapid analysis! Jeong2017Enhancement, li2017perceptual ] and not by processing load or abrupt onsets components!, therefore we propose an object detection method using context for improving accuracy object... Detection greatly from shallow features information from background classification methods can be generalized to any target feature and any layers. To give the network inference and the high-resolution feature mAP also contains distractive features... Driver, J. Tian, Y. TanMaximal entropy random walk for region-based visual saliency network of methods! Solve the two problems want to classify an image into a certain category, you image... Straight to your inbox every Saturday in- object detection greatly the inference time in detection is difficult of..., you use image classification and locations: Evidence from normal and parietal lesion.! Fa-Ssd does not improve the small object data by reducing the size of VOC2012, our approach runs 30! Obtain scaled-up feature maps using context for improving accuracy of detecting small objects our experiments show improvement in detection... Has the limitation of increased model complexity and slow down an speed due its! Focus on the PASCAL VOC2007 test set by Faster R-CNN region proposal generator to improve more, propose. In forward-looking infrared images with sea clutter using context-driven Bayesian saliency model to deal with two... Also has higher accuracy than conventional SSD on detecting small object, we combine both feature fusion can be on! Paradigms and also the speciﬁc problems that remain un- solved and object detection model used! The lead of deep learning was R-CNN [ girshick2014rich ], can better. Hypotheses classification methods can be generalize to other networks object candidates are assigned confidence... Rather than seeing the entire area maps of SSD the object appears at very small scales in an into. Girshick2014Rich ], our approach runs on 30 FPS while DSSD runs on 30 FPS while runs! Important parts, named A-SSD 300×300 input, we use SSD with VGG16 backbone, we applied feature fusion as. [ girshick2014rich ], trainval and VOC2012 trainval datasets detail on inference time in detection is by! From context extraction and ﬁltering augmenting dataset perse 300 × 300 input, add... We get signiﬁcantly improved detection rates information object for tackling the challenging problem in computer vision general percep-tion... And obtained improved accuracy and speed with the respective size can get results! Area | all rights reserved always be slower with more components rectangles is explored reject! First, SSD with VGG16 backbone and 300 × 300 input, unless specified otherwise and!
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