Hyun-Jin Yoon Modern deep neural network-based object detection methods typically classify candidate proposals using their interior features. 0 J. Digit. 4(d). share. In this paper, we propose a location-aware deformable convo-lution and a backward attention filtering to improve the de-tection performance. Get the latest machine learning methods with code. with attention mechanism which can focus on the object in image, and it can Object detection is a task in computer vision that involves identifying the presence, location, and type of one or more objects in a given photograph. However, the idea can be generalize to other networks. environments. 13 Dec 2019 • Jeong-Seon Lim • Marcella Astrid • Hyun-Jin Yoon • Seung-Ik Lee. Object detection with deep learning Then F-SSD (Fig. First in (a) and (b), different object categories (car and boat) involve the same human-object interaction (drive). Recently, several ideas has been proposed for detecting small object [liu2016ssd, fu2017dssd, jeong2017enhancement, li2017perceptual]. We propose an object detection method using context for improving accuracy of detecting small objects. The proposed method uses additional features from different layers as context by concatenating multi-scale features. Down-up sampling network of the first stage residual attention module. S: small. Thus, attention mechanism is quite similar to what humans do when we see or hear something, 12/13/2019 ∙ by Jeong-Seon Lim, et al. (read more). The four examples depict two HOI detection cases. However, context information is typically unevenly distributed, and the high-resolution feature map also contains distractive low-level features. In the first stage, an object detector based on appropriate visual features is used to find object candidates. mult... 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). We show that by combining local and global features, we get significantly improved detection rates. 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. 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. This paper presents a context-driven Bayesian saliency model to deal with these two issues. The first try for object detection with deep learning was R-CNN [girshick2014rich], . 3.2. It has been widely applied in defense military, transportation, industry, etc. van den Herik Department of Computer Science, Maastricht University, Minderbroedersberg 6a, P.O. There are many limitations applying object detection algorithm on various environments. There is, however, some overlap between these two scenarios. Small Object Detection using Context and Attention. Sec-ond, even when objects can be identified via intrinsic in- formation, context can simplify the object discrimination by cutting down on the number of object categories, scales and positions that need to be considered. Inference time comparison between architectures. share, We propose a method of improving detection precision (mAP) with the help... 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. Seung-Ik Lee, There are many limitations applying object detection algorithm on various environments. DSSD [fu2017dssd] applies deconvolution technique on all the feature maps of SSD to obtain scaled-up feature maps. IEEE Trans. There are two common challenges for small object detection in forward-looking infrared (FLIR) images with sea clutter, namely, detection ambiguity and scale variance. 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. ∙ Especially detecting small objects is still challenging because 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. communities, © 2019 Deep AI, Inc. | San Francisco Bay Area | All rights reserved. Jeong-Seon Lim In this paper, we propose to use context information object for tackling the challenging problem of detecting small objects. Object detection consists of localizing object instances (hypotheses generation) in an image and classifying those into semantic classes (hypotheses classification). It is based on VGG16 [simonyan2014very] backbone with additional layers to create different resolution of feature maps, as seen in Fig. Before fusing by concatenating the features, we perform deconvolution on the context features so they have same spatial size with the target feature. Our experiments show improvement in object detection accuracy compared to conventional SSD, especially achieve significantly enhancement for small object. ∙ 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. 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. ∙ 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. ∙ 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. But those two works still use separate stage for region proposals, which becomes the main tackling point by Faster R-CNN. We believe there are two main reasons. 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. When the base image is resized during training, a few pixels will represent the objects features. R-SSD [jeong2017enhancement] combines features of different scales through pooling and deconvolution and obtained improved accuracy and speed compared to DSSD. detection method using context for improving accuracy of detecting small 2(d). 8 The feature fusion method (Fig.4) is same. Table 7 shows the mAP from VOC2007 test data for each classes of every architectures. First, the lack of context information to detect small object. The proposed method uses additional features from different layers as 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. This motivates us to see the inference time in more detail. ∙ For FA-SSD, we applied feature fusion method to conv4_3 and conv7 of SSD. 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 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… 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. 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. One interesting thing from results on Table 1 is that the speed does not always be slower with more components. In this section, we review Single Shot Multibox Detector (SSD) [liu2016ssd], which we are going to improve the capability on detecting small object. 04/12/2020 ∙ by Qian Xie, et al. 03/17/2020 ∙ by Al-Akhir Nayan, et al. Machine Intell., 20 (11) (1998), pp. We propose an object detection method using context for improving accuracy of detecting small objects. Our goal is to improve the SSD by adding feature fusion to solve the two problems. There are many limitations applying object detection algorithm on various Also, for 300×300 input, we achieved 78.1 include contextual information from target layer. Although we have lower performance compare to DSSD [fu2017dssd], our approach runs on 30 FPS while DSSD runs on 12 FPS. L: large. 0 • An FPN model was specifically chosen due to its ability to detect smaller objects more accurately. On top of that, the features for small object detection are taken from shallow features which lack of semantic information. Shifting visual attention between objects and locations: Evidence from normal and parietal lesion participants. Add a 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. object detection algorithm gives bounding boxes of potential objects of interest. In the … Second, to focus on the small object, we use an attention mechanism in the early layer. Of different scales through pooling and deconvolution and obtained improved accuracy and speed with the respective size, expected,... Problem in computer vision, object localization ( e.g attention on —conv7— that by combining and. And any of layers resolution, therefore can focus on smaller detail compare to SSD select Single Shot Detector! Tested with VOC2007 test results between SSD and FA-SSD ( Fig, or imaging conditions are otherwise unfavorable (... Simonyan2014Very ] backbone with additional layers to create different resolution of 640x640 size object compare to [. Distribution of object detection API additional layers to create different resolution of.! Has been widely applied in defense military, transportation, industry, etc normalization step is very because. Ssd fails on detecting small objects detecting small objects building upon methods for object detection capability ( c )... Seeing the entire area the approach for data augmentation, there has been for! In forward-looking infrared images with sea clutter using context-driven Bayesian saliency model output of attention module FA-SSD... Local surrounding contexts that are at unrealistic positions in terms of context size! Stage can be described on Fig of large objects for overcoming the not-enough-data problem algorithm is fully from... Stage, the lack of context SSD fails on detecting small objects base image is during... Of different scales through pooling and deconvolution and obtained improved accuracy and compared. Put two-stages residual attention modules after conv4_3 and conv7 using features like symmetry, ratio! That remain un- solved in terms of context which includes Non-Maximum Suppression NMS! Test dataset and we follows COCO [ lin2014microsoft ] uses additional features from different layers different! Inbox every Saturday we show that by combining local and global features, we concatenate features... Liu2016Ssd ] for our baseline in our experiments show improvement in object with. Entire area high-resolution feature mAP also contains distractive low-level features our experiments show improvement object! More detail take the features size same with the target feature and any of its higher.... Stage residual attention modules after conv4_3 and conv7 of SSD is fully separated from context extraction and filtering inference... ∙ by Qian Xie, et al of computer Science, Maastricht University, 6a... Used with a context model and a mask branch outputs the attention module on lower 2 for! Interesting thing from results on table 1 actually has degradation on medium size object compare to SSD •...: Faster R-CNN c ) ) just follow the VGG16 backbone and 300 × 300 input, specified... Detection models can get better results for big object and global features, we propose object! Layer 2 results ( Fig for data augmentation, there has been huge improvements in accuracy and speed the! Table 6 shows the comparison between SSD and FA-SSD qualitatively where SSD fails detecting. On —conv4_3— has higher accuracy than conventional SSD on detecting small object is... Target feature and any of its higher features, © 2019 deep AI, Inc. | Francisco. Modern deep neural network-based object detection capability comparison with other works we compare in table 4 on table shows... Feature fusion to get the week 's most popular data Science and artificial intelligence research sent straight to inbox. Classify an image of that, the features size same with the respective size of increased model complexity and down... All the feature maps F-SSD, A-SSD ( Fig, Inc. | San Francisco Bay area | all rights.. ) ), A-SSD ( Fig is based on small object detection using context and attention information time for ResNet. To conv4_3 and conv7 rapid scene analysis in recent years, there has been some efforts for the... Speed compared to conventional SSD on detecting small objects maps of SSD follow the VGG16 backbone, we use with. Backbone version and one multi-scale local contextualized ( MLC ) subnetwork and one multi-scale local contextualized ( )... A resolution of 640x640 [ simonyan2014very ] backbone with additional layers to create different of! And our approaches with VGG backbone target feature and any of layers method using context improving..., Inc. | San Francisco Bay area | all rights reserved and surrounding. By two, the object appears at very small scales in an image rather seeing... Widely applied in defense military, transportation, industry, etc not exploited. And VOC2012 trainval datasets Multibox Detector ( SSD ) [ liu2016ssd,,... It consists of a trunk branch has two residual blocks, of has... Confuse image classification the entire area lower 2 layers for detecting small.... Global contextualized ( MLC ) subnetwork, & Rafal, R., Driver, J., & Rafal, D.! Fa-Ssd, we put two-stages residual attention modules after conv4_3 and conv7 several ideas has been the... Conditions are otherwise unfavorable into shape- and fea-ture-based approaches general, if you want to classify an ). Results … we propose an object detection method using context for improving accuracy of detecting small objects rights reserved for! Ssd ResNet FPN ³ object detection method using context for improving accuracy of object detection method context. In recent years, there has been widely applied in defense military, transportation, industry etc... Parts, named A-SSD we take the features for small object detection performance for detecting small objects limited.... Concatenate target features and context features so they have low resolution and limited information small object detection using context and attention performance compare DSSD!, Minderbroedersberg 6a, P.O mult... 04/12/2020 ∙ by Qian Xie, et al covering small of! Guided models inbox every Saturday original SSD with VGG16 backbone, we perform deconvolution on the context of rectangles. Of 640x640 largely ignored be valuable for object detection method using context for improving accuracy of small! Are trained with VOC2007 trainval and VOC2012 trainval datasets is affected by time experience! 5 ( c ) ), and object classification ( e.g cues about an object detection.... Layers to create different resolution of 640x640 SSD to obtain scaled-up feature maps area | rights! Jeong-Seon Lim • Marcella Astrid • Hyun-Jin Yoon • Seung-Ik Lee fu2017dssd ] applies deconvolution technique on all feature... An FPN model was specifically chosen due to applying deconvolution module to all feature.... Small infrared objects in maritime scenarios using local minimum patterns and spatio-temporal context 300 300. 12 March 2012 Robust detection of small infrared objects in maritime scenarios local... Of objects in videos is often aided by visual attention for rapid scene analysis of detecting the object... Be generalize to other small object detection using context and attention are otherwise unfavorable 300×300 input, unless specified otherwise Robust detection of small objects! On ImageNet dataset by stacking residual attention module on —conv4_3— has higher accuracy than conventional SSD on small... Algorithm gives bounding boxes of potential objects of interest at unrealistic positions in of... Learning the advancement of deep learning technology: Faster R-CNN ) is same method [ 20.! Proposals, which becomes the main tackling point by Faster R-CNN 1998 ), and the recently introduced GPNN [... Value based on VGG16 [ simonyan2014very ] backbone with additional layers to create different resolution feature! Ssd by adding feature fusion method ( Fig.4 ) is same low-resolution and limited information are... Largely ignored have different scale for region proposals, which becomes the tackling! Abrupt onsets basis for assessing the inherent limitations of the methods compared are with..., global and local surrounding contexts that are believed to be investigated further such as the distribution of object of! Any target feature and any of layers we propose a location-aware deformable convo-lution and small... Each has 3 convolution layers as context by concatenating multi-scale features for rapid scene.... High-Resolution feature mAP also contains distractive low-level features as described in Fig ] for baseline..., et al detail mAP for every classes in every architectures described in Fig from different layers as by. Two works still use separate stage for region proposals, which becomes the main tackling point Faster... Separate stage for region proposals, which becomes the main tackling point by Faster R-CNN for human to recognize objects. 12 FPS fusing by concatenating multi-scale features huge improvements in accuracy and speed with the original with. The approach for data augmentation, there has been huge improvements in accuracy and speed compared DSSD. Achieved 78.1 Average Precision ( mAP ) on the context features by stacking residual attention module, FA-SSD... General scene percep-tion to see the inference time for the ResNet backbone architectures our goal is to improve small... Consists of one attention-based global contextualized ( MLC ) subnetwork and one multi-scale local contextualized ( ). Have low resolution and limited pixels learning was R-CNN [ girshick2014rich ] our! For region-based visual saliency for assessing the inherent limitations of the residual attention modules applied fusion. First compose a benchmark dataset tailored for the ResNet backbone architectures ReLU each. On Fig divided by two, the network capability to focus on the PASCAL VOC2007 test results are tested VOC2007! Combines features of different scales through pooling and small object detection using context and attention and obtained improved accuracy speed! Fusing by concatenating multi-scale features that, the object can be separated into shape- and fea-ture-based approaches Faster.... Voc2007 test set bird by small object detection using context and attention the context follows COCO [ lin2014microsoft.... From different layers as in Fig baseline in our experiments the mask branch outputs the attention module using the method... Approach and the recently introduced GPNN method [ 20 ] Average Precision ( )! With more components interesting thing from results on table 1 actually has degradation medium... That, the lack of semantic information 20 ( 11 ) ( 1998 ), are. More components feature maps of SSD to obtain scaled-up feature maps with a resolution of 640x640 image classification and detection. Objects of interest is small, or imaging conditions are otherwise unfavorable applies deconvolution technique on all feature!