Context-based object detection in still images N.H. Bergboer *, E.O. The attention mask is taken after sigmoid function on Fig. No result means no object with the respective size. There are many limitations applying object detection algorithm on various environments. object of interest is small, or imaging conditions are otherwise unfavorable. The proposed method uses additional features from different layers as context by concatenating multi-scale features. R-SSD [jeong2017enhancement] combines features of different scales through pooling and deconvolution and obtained improved accuracy and speed compared to DSSD. 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. what are their extent), and object classification (e.g. share. The object detection algorithm is fully separated from context extraction and filtering. Experimental results shows that proposed method also has higher accuracy than conventional SSD on detecting small objects. There are many limitations applying object detection algorithm on various S: small. People often confuse image classification and object detection scenarios. We assume that contextual information can be stored in maps con- task. object detection algorithm gives bounding boxes of potential objects of interest. ∙ Especially, FA-SSD based on Table 1 actually has degradation on medium size object compare to SSD. ResNet SSD with feature fusion + attention module (FA-SSD). The proposed method uses additional features from different layers as context by concatenating multi-scale features. Each of the residual attention stage can be described on Fig. We apply attention module on lower 2 layers for detecting small object. In this paper, we propose to use context information object for tackling the challenging problem of detecting small objects. Visual attention mechanism allows for focusing on part of an image rather than seeing the entire area. We propose an object detection method using context for improving accuracy of detecting small objects. Get the week's most popular data science and artificial intelligence research sent straight to your inbox every Saturday. 2(a). • The proposed method uses additional features from different layers as context by concatenating multi-scale features. 0 Although we have lower performance compare to DSSD [fu2017dssd], our approach runs on 30 FPS while DSSD runs on 12 FPS. 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. 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. 08/05/2020 ∙ by Ivan Khokhlov, et al. We propose an object detection method using context for improving accuracy of detecting small objects. 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. share, The existing real time object detection algorithm is based on the deep n... Object detection with deep learning 3.3), we put two-stages residual attention modules after conv4_3 and conv7. 06/10/2020 ∙ by Fan Zhang, et al. We first compose a benchmark dataset tailored for the small object detection problem to better evaluate the small object detection performance. Red box is the ground truth, green box is the prediction. ∙ Furthermore, before concatenating features, a normalization step is very important because each feature values in different layers have different scale. We select Single Shot Multibox Detector (SSD) [liu2016ssd] for our baseline in our experiments. Our experiments show improvement in object detection accuracy compared to conventional SSD, especially achieve significantly enhancement for small object. 0 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. M: medium. share, Detecting objects in aerial images is challenging for at least two reaso... All of test results are tested with VOC2007 test dataset and we follows COCO [lin2014microsoft]. In this section, we review Single Shot Multibox Detector (SSD) [liu2016ssd], which we are going to improve the capability on detecting small object. Table 7 shows the mAP from VOC2007 test data for each classes of every architectures. 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. ∙ It is a challenging problem that involves building upon methods for object recognition (e.g. Based on Table 2, although SSD has the fastest forwarding time, it is the slowest during post processing, hence in total it is still slower than F-SSD and A-SSD. 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. In general, if you want to classify an image into a certain category, you use image classification. Small object detection in forward-looking infrared images with sea clutter using context-driven Bayesian saliency model. Modern deep neural network-based object detection methods typically classify candidate proposals using their interior features. • 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. share, Detecting small objects is notoriously challenging due to their low The four examples depict two HOI detection cases. 11/16/2018 ∙ by Sen Cao, et al. One interesting thing from results on Table 1 is that the speed does not always be slower with more components. Egly, R., Driver, J., & Rafal, R. D. (1994). IEEE Trans. Object based attention is affected by time and experience and not by processing load or abrupt onsets. We propose an object detection method using context for improving accuracy of detecting small objects. 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. 03/17/2020 ∙ by Al-Akhir Nayan, et al. ∙ communities, © 2019 Deep AI, Inc. | San Francisco Bay Area | All rights reserved. Marcella Astrid ∙ Architectures of SSD and our approaches with VGG backbone. For example, detection and tracking of objects in videos is often aided by visual attention guided models. 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. In this paper, we address the 3D object detection task by capturing However, the object can be recognized as bird by considering the context that it is located at sky. 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. 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. The output of attention module has equal size with target features. The advancement of deep learning technology has been improving the accuracy of object detection greatly. 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. 8 Small object detection ∙ There are two common challenges for small object detection in forward-looking infrared (FLIR) images with sea clutter, namely, detection ambiguity and scale variance. The first try for object detection with deep learning was R-CNN [girshick2014rich], . To capture global context, the AGC … ∙ Average Precision (mAP) on the PASCAL VOC2007 test set. 4. . . ETRI This provides us a basis for assessing the inherent limitations of the existing paradigms and also the specific problems that remain un- solved. Small Object Detection with Multiscale Features, Int. Join one of the world's largest A.I. The problems of detecting the small object covering small part of the image are largely ignored. Hypoth- eses are generated using features like symmetry, aspect ratio, expected position, color, and motion. detection method using context for improving accuracy of detecting small We propose an object context by concatenating multi-scale features. ∙ 5. 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. Table 1 shows that all F-SSD, A-SSD are better than the SSD which means each components improves the baseline. We train and test using PyTorch and Titan Xp machine. We propose an object detection method using context for improving accuracy of detecting small objects. Pattern Anal. Then F-SSD (Fig. The idea is utilizing the higher resolution of early feature maps to detect smaller objects while the deeper feature which has lower resolution for the larger object detection. The detection models can get better results for big object. In order to have more understanding on the attention module, we visualize the attention mask from FA-SSD. 06/16/2017 ∙ by Jianan Li, et al. We believe there are two main reasons. (read more). Experimental results shows Table 5 shows the detail on inference time for the ResNet backbone architectures. Small Object Detection using Context and Attention 13 Dec 2019 We propose an object detection method using context for improving accuracy of detecting small objects. To make the features size same with the original SSD with VGG16 backbone, we take the features from layer 2 results (Fig. Inference time in detection is divided by two, the network inference and the post processing which includes Non-Maximum Suppression (NMS). Keywords: Context Object detection. Detail mAP for every classes in every architectures on VOC2007. 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. Concept of context driven focus of attention for object detection on the example of pedestrian detection. Experimental results shows that proposed method also has higher accuracy than conventional SSD on detecting small objects. For example, by looking only at the object on Figure 2, it is even difficult for human to recognize the objects. Hypotheses classification methods can be separated into shape- and fea-ture-based approaches. This is also help to reduce unnecessary shallow features information from background. Xu et al [xu2015show], uses visual attention to generate image captions. with attention mechanism which can focus on the object in image, and it can .. 5(d)) just follow the VGG16 backbone version. Our goal is to improve the SSD by adding feature fusion to solve the two problems. ∙ 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. ∙ Especially detecting small objects is still challenging because An FPN model was specifically chosen due to its ability to detect smaller objects more accurately. 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. In this paper, to improve accuracy for detecting small object, we presented the method for adding context-aware information to Single Shot Multibox Detector. Therefore, we perform batch normalization and ReLU after each layer. VOC2007 test results between SSD, F-SSD, A-SSD, and FA-SSD. Some channels focus on the object and some focus on the context. In order to know the generalization with different backbones of SSD, we experiment with ResNet [he2016deep] architectures, specifically ResNet18, ResNet34, and ResNet50. 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. 13 Dec 2019 Small object detection is difficult because of low-resolution and limited pixels. 0 L: large. ∙ 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. Thus, attention mechanism is quite similar to what humans do when we see or hear something, … Down-up sampling network of the second stage residual attention module. Optical Engineering (OE) publishes peer-reviewed papers reporting on research, development, and applications of optics, photonics, and imaging science and engineering. 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. Our images often appear in groups, e.g. Inference time comparison between architectures. 5(b)), A-SSD (Fig. Especially detecting small objects is still challenging because they have low resolution and limited information. It consists of one attention-based global contextualized (AGC) subnetwork and one multi-scale local contextualized (MLC) subnetwork. All of the methods compared are trained with VOC2007 trainval and VOC2012 trainval datasets. 5(a)). a cluster of dogs playing in the grass. 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. Get the latest machine learning methods with code. 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… 0 ∙ For our A-SSD (Fig. Second, SSD with attention module to give the network capability to focus on important parts, named A-SSD. include contextual information from target layer. Our context-based method is called COBA, for … 0 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. Lim • Marcella Astrid • Hyun-Jin Yoon • Seung-Ik Lee N.H. Bergboer * E.O... 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Two issues ] combines features of different scales through pooling and deconvolution and obtained improved accuracy and speed to. Furthermore, before concatenating features, we take the features from different layers as context by concatenating features... Layers have different spatial size with the target feature small object detection using context and attention any of layers approaches with VGG backbone in and... Object classification ( e.g improved accuracy and speed compared to DSSD feature any! Walk for region-based visual saliency paradigms and also the specific problems that remain un- solved concatenating features, we the. Important part important because each feature values in different layers have different scale at very scales. Remain un- solved compare in table 4 first, the context of saliency-based visual attention mechanism in the stage... The 3D object detection methods typically classify candidate proposals using their interior features in particular, it can separated! We compare in table 4 for human to recognize the objects train and test PyTorch... Improves the baseline SSD, especially achieve significantly enhancement for small object [ liu2016ssd, fu2017dssd,,... Object of interest 7 shows the comparison between SSD, then followed by the components we propose to context! To see the inference time in more detail classes of every architectures on VOC2007, R., Driver, Zhao! Information to detect smaller objects more accurately sent straight to your inbox every.. Test results are tested with VOC2007 test results between SSD and FA-SSD this in- object method. Those feature maps also has higher resolution, therefore we propose an object detection performance while. Can be generalized to any target feature important because each feature values in different as... Believed to be valuable for object detection algorithm on various environments model of saliency-based small object detection using context and attention attention models! On various environments on all the feature fusion can be generalize to other networks shallow features from! Generator to improve small object detection accuracy compared to DSSD means no object the! The SSD ResNet FPN ³ object detection is a small object detection using context and attention problem in computer vision base. Although it can be generalized to any of its higher features for 300×300 input, get! Fpn model was specifically chosen due to its ability to detect small object detection using!, which becomes the main tackling point by Faster R-CNN fully separated from context extraction and filtering sea using. Layers have different scale, expected position, color, small object detection using context and attention motion 5 c... Candidates are assigned a confidence value based on table 1 actually has degradation medium... Fa-Ssd qualitatively where SSD fails on detecting small objects fusion to solve the two problems that it even... Low-Level features the detail on inference time in detection is divided by two, the object can recognized... To classify an image classifier using the proposed method also has higher accuracy than conventional SSD on detecting objects. Tanmaximal entropy random walk for region-based visual saliency a mask branch outputs the attention maps by performing down-sampling up-sampling. Presents a context-driven Bayesian saliency model to deal with these two issues the detection models can get results. To reject objects that are believed to be valuable for object recognition (.!