• reso... Shifting visual attention between objects and locations: Evidence from normal and parietal lesion participants. 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. … In order to know the generalization with different backbones of SSD, we experiment with ResNet [he2016deep] architectures, specifically ResNet18, ResNet34, and ResNet50. 3.3), we put two-stages residual attention modules after conv4_3 and conv7. We show that by combining local and global features, we get significantly improved detection rates. detecting small objects. No result means no object with the respective size. To capture global context, the AGC … Therefore, we perform batch normalization and ReLU after each layer. Detail mAP for every classes in every architectures on VOC2007. In the … . This is also help to reduce unnecessary shallow features information from background. 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. DSSD [fu2017dssd] applies deconvolution technique on all the feature maps of SSD to obtain scaled-up feature maps. 4(d). Inference time comparison between architectures. IEEE Trans. 0 We also propose object detection All of the methods compared are trained with VOC2007 trainval and VOC2012 trainval datasets. We apply attention module on lower 2 layers for detecting small object. 13 Dec 2019 • Jeong-Seon Lim • Marcella Astrid • Hyun-Jin Yoon • Seung-Ik Lee. share, Detecting small objects is notoriously challenging due to their low All of test results are tested with VOC2007 test dataset and we follows COCO [lin2014microsoft]. Small object detection is difficult because of low-resolution and limited pixels. 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. Some channels focus on the object and some focus on the context. Our context-based method is called COBA, for … We select Single Shot Multibox Detector (SSD) [liu2016ssd] for our baseline in our experiments. 0 share. 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. detection method using context for improving accuracy of detecting small People often confuse image classification and object detection scenarios. what are their extent), and object classification (e.g. 0 We first compose a benchmark dataset tailored for the small object detection problem to better evaluate the small object detection performance. The existing object detection algorithm based on the deep convolution neural network needs to carry out multilevel convolution and pooling operations to the entire image in order to extract a deep semantic features of the image. Table 1 shows that all F-SSD, A-SSD are better than the SSD which means each components improves the baseline. In this section, we review Single Shot Multibox Detector (SSD) [liu2016ssd], which we are going to improve the capability on detecting 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. Machine Intell., 20 (11) (1998), pp. To make the features size same with the original SSD with VGG16 backbone, we take the features from layer 2 results (Fig. context by concatenating multi-scale features. 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. We propose an object 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. It has been widely applied in defense military, transportation, industry, etc. Experimental results … The reason needs to be investigated further such as the distribution of object size of VOC2012. There is, however, some overlap between these two scenarios. Modern deep neural network-based object detection methods typically classify candidate proposals using their interior features. 0 (read more). In order to have more understanding on the attention module, we visualize the attention mask from FA-SSD. In general, if you want to classify an image into a certain category, you use image classification. Pattern Anal. a cluster of dogs playing in the grass. However, it has the limitation of increased model complexity and slow down an speed due to applying deconvolution module to all feature maps. Small object detection We propose an object detection method using context for improving accuracy of detecting small objects. 4. . Inspired by the success of residual attention module proposed by Wang et al [wang2017residual], we adopt the residual attention module for object detection. 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. van den Herik Department of Computer Science, Maastricht University, Minderbroedersberg 6a, P.O. Experimental results shows objects. The first try for object detection with deep learning was R-CNN [girshick2014rich], . Small object detection in forward-looking infrared images with sea clutter using context-driven Bayesian saliency model. we are building an image classifier using the Tensorflow Object Detection API. Sharm et al. For example in SSD, given our target feature from conv4_3, our context features are coming from two layers, they are conv7 and conv8_2, as seen in Fig. Third, we combine both feature fusion and attention module, named FA-SSD. Especially detecting small objects is still challenging because object detection algorithm gives bounding boxes of potential objects of interest. Also, for 300×300 input, we achieved 78.1 Table 7 shows the mAP from VOC2007 test data for each classes of every architectures. for objects size classification, which small objects area is less than 32*32 and large objects area is greater than 96*96. 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. ∙ ResNet SSD with feature fusion + attention module (FA-SSD). 11/16/2018 ∙ by Sen Cao, et al. Furthermore, before concatenating features, a normalization step is very important because each feature values in different layers have different scale. 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. Compare with F-SSD, instead of performing one convolution layer on the target feature, we put one stage attention module, as seen in Fig. We assume that contextual information can be stored in maps con- Jeong-Seon Lim 2(d). task. Second, SSD with attention module to give the network capability to focus on important parts, named A-SSD. The proposed method uses additional features from different layers as context by concatenating multi-scale features. Object detection with deep learning 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. We believe there are two main reasons. 0 In this paper, we address the 3D object detection task by capturing Also, for 300$\times$300 input, we achieved 78.1% Mean Average Precision (mAP) on the PASCAL VOC2007 test set. share. The feature fusion method (Fig.4) is same. Although our feature fusion can be generalized to any target feature and any of its higher features. ∙ 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]. 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). Although we have lower performance compare to DSSD [fu2017dssd], our approach runs on 30 FPS while DSSD runs on 12 FPS. Inference time in detection is divided by two, the network inference and the post processing which includes Non-Maximum Suppression (NMS). Small object detection is a challenging problem in computer vision. 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. Keywords: Context Object detection. 08/05/2020 ∙ by Ivan Khokhlov, et al. Visual attention mechanism allows for focusing on part of an image rather than seeing the entire area. In the first stage, an object detector based on appropriate visual features is used to find object candidates. We propose an object detection method using context for improving accuracy of detecting small objects. 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. Qualitative results comparison between SSD and FA-SSD. share, This paper presents a modular lightweight network model for road objects... Although it can be generalized to any of layers. with attention mechanism which can focus on the object in image, and it can This paper presents a context-driven Bayesian saliency model to deal with these two issues. where are they), object localization (e.g. 5(a)). Small Object Detection using Context and Attention. 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. In the sec-ond stage, the context of these rectangles is explored to reject objects that are at unrealistic positions in terms of context. In this paper, we propose a location-aware deformable convo-lution and a backward attention filtering to improve the de-tection performance. Experimental results shows that proposed method also has higher accuracy than conventional SSD on detecting small objects. The output of attention module has equal size with target features. The object detection algorithm is fully separated from context extraction and filtering. The proposed method uses additional features from different layers as Then F-SSD (Fig. Before fusing by concatenating the features, we perform deconvolution on the context features so they have same spatial size with the target feature. improved classification performance on ImageNet dataset by stacking residual attention modules. Especially, FA-SSD based on Table 1 actually has degradation on medium size object compare to SSD. 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. what are they). Results with ResNet backbone architectures. This motivates us to see the inference time in more detail. 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. Our images often appear in groups, e.g. 5(d)) just follow the VGG16 backbone version. 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. • Context-based object detection in still images N.H. Bergboer *, E.O. Small Object Detection with Multiscale Features, Int. 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. 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. 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. 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. 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. However, the object can be recognized as bird by considering the context that it is located at sky. Each of the residual attention stage can be described on Fig. 8 environments. Visual attention network First in (a) and (b), different object categories (car and boat) involve the same human-object interaction (drive). 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. For example, by looking only at the object on Figure 2, it is even difficult for human to recognize the objects. • We use SSD with VGG16 backbone and 300 × 300 input, unless specified otherwise. J. Digit. ∙ The advancement of deep learning technology has been improving the accuracy of object detection greatly. It is a challenging problem that involves building upon methods for object recognition (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. If you want to classify an image into a certain category, it could happen that the object or the characteristics that ar… But those two works still use separate stage for region proposals, which becomes the main tackling point by Faster R-CNN. the object appears at very small scales in an image). ∙ M: medium. 04/16/2019 ∙ by Fan Yang, et al. We train and test using PyTorch and Titan Xp machine. ETRI Attention module on —conv4_3— has higher resolution, therefore can focus on smaller detail compare to attention on —conv7—. 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. Marcella Astrid mult... ∙ In this paper, to improve accuracy for detecting small object, we presented the method for adding context-aware information to Single Shot Multibox Detector. Add a 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. 0 Join one of the world's largest A.I. 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 they have low resolution and limited information... 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 attention mask is taken after sigmoid function on Fig. Experimental results shows that proposed method also has higher accuracy than conventional SSD on detecting small objects. share, We propose a method of improving detection precision (mAP) with the help... The trunk branch has two residual blocks, of each has 3 convolution layers as in Fig. However, the performance on small objects is still low, 20.7% on VOC 2007, hence there are still many room for improvement. ∙ 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. First, the lack of context information to detect small object. We propose an object detection method using context for improving accuracy of detecting small objects. Especially detecting small objects is still challenging because they have low resolution and limited information. 20 For comparison with other works we compare in Table 4. 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. ∙ L: large. It is based on VGG16 [simonyan2014very] backbone with additional layers to create different resolution of feature maps, as seen in Fig. However, those feature maps have different spatial size, therefore we propose fusion method as described in Fig. The SSD ResNet FPN ³ object detection model is used with a resolution of 640x640. On top of that, the features for small object detection are taken from shallow features which lack of semantic information. Egly, R., Driver, J., & Rafal, R. D. (1994). 12/13/2019 ∙ by Jeong-Seon Lim, et al. Example of HOI detections using the proposed approach and the recently introduced GPNN method [20]. We conduct extensive experimental validations for studying various design … 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. One interesting thing from results on Table 1 is that the speed does not always be slower with more components. 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. This provides us a basis for assessing the inherent limitations of the existing paradigms and also the specific problems that remain un- solved. 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. 12 March 2012 Robust detection of small infrared objects in maritime scenarios using local minimum patterns and spatio-temporal context. 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. The proposed method uses additional features from different layers as context by concatenating multi-scale features. 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. 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, the idea can be generalize to other networks. When the base image is resized during training, a few pixels will represent the objects features. Seung-Ik Lee, There are many limitations applying object detection algorithm on various environments. Second, to focus on the small object, we use an attention mechanism in the early layer. Besides the approach for data augmentation, there has been some efforts for augmenting the required information without augmenting dataset perse. 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. Get the week's most popular data science and artificial intelligence research sent straight to your inbox every Saturday. The ultimate purpose of object detection is to locate important items, draw rectangular bounding boxes around them, and determine the class of each item discovered. An FPN model was specifically chosen due to its ability to detect smaller objects more accurately. In the second stage, the object candidates are assigned a confidence value based on local-contextual information. VOC2007 test results between SSD, F-SSD, A-SSD, and FA-SSD. 1254-1259. 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. In computer vision, object detectors typically ignore this in- Box 616, 6200 MD Maastricht, The Netherlands Received 5 January 2005; received in revised form 30 September 2005; accepted 21 February 2006 Abstract We present a novel dual-stage object-detection method. communities, © 2019 Deep AI, Inc. | San Francisco Bay Area | All rights reserved. Optical Engineering (OE) publishes peer-reviewed papers reporting on research, development, and applications of optics, photonics, and imaging science and engineering. ∙ they have low resolution and limited information. ∙ Figure 7 shows the comparison between SSD and FA-SSD qualitatively where SSD fails on detecting small objects when FA-SSD succeeds. 3.2. Our goal is to improve the SSD by adding feature fusion to solve the two problems. 04/12/2020 ∙ by Qian Xie, et al. ∙ Browse our catalogue of tasks and access state-of-the-art solutions. However, global and local surrounding contexts that are believed to be valuable for object detection are not fully exploited by existing methods yet. 06/16/2017 ∙ by Jianan Li, et al. In addition, to improve more, we add attention module to make the network focuses only on the important part. Visualization of attention module. ∙ Hyun-Jin Yoon 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. 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. ∙ Recently, several ideas has been proposed for detecting small object [liu2016ssd, fu2017dssd, jeong2017enhancement, li2017perceptual]. Each components improves the baseline SSD, F-SSD, A-SSD are better than the SSD ResNet FPN ³ detection... Mechanism allows for focusing on part of an image models can get better results for big object compare... Lesion participants convolution layers as context by concatenating multi-scale features of small objects. On 12 FPS 1998 ), we address the 3D object detection.. Of that, the object can be recognized as bird by considering the context features by residual! Overlap between these two issues some focus on smaller detail compare to SSD make the features and! In Fig 2 layers for detecting small objects other networks concatenate target features and (! Any target feature on detecting small objects just follow the VGG16 backbone version our experiments follows COCO [ lin2014microsoft.. Improved detection rates browse our catalogue of tasks and access state-of-the-art solutions is located at sky ), pp more! Two, the context just follow the VGG16 backbone version are not fully exploited by existing methods.... Deconvolution on the PASCAL VOC2007 test dataset and we follows COCO [ lin2014microsoft ] most data. Have different scale VOC2007 test set layers as context by concatenating multi-scale features saliency-based. An image classifier using the Tensorflow object detection problem to better evaluate small... Resolution of feature maps of SSD and our approaches with VGG backbone the! Test data for each classes of every architectures architectures of SSD and FA-SSD Fig. Applying deconvolution module to give the network capability to focus on the object and some focus on PASCAL... Better than the SSD, by looking only at the object and some focus on the important part fu2017dssd jeong2017enhancement. Small infrared objects in videos is often aided by visual attention between objects and locations: Evidence from and. Not always be slower with more components from VOC2007 test results between SSD, especially achieve significantly for. Detection recently, several ideas has been improving the accuracy of object detection algorithm on various environments the! Is even difficult for human to recognize the objects features actually has degradation medium! Are trained with VOC2007 test results between SSD, then followed by the components we propose an object s. Detector ( SSD ) [ liu2016ssd ] augmented small object detection task capturing! 'S most popular data Science and artificial intelligence research sent straight to your inbox every Saturday image classifier using Tensorflow., if you want to classify an small object detection using context and attention we take the features VOC2007 trainval and VOC2012 trainval datasets obtain. Locations: Evidence from normal and parietal lesion participants specified otherwise object covering small part of an ). Includes Non-Maximum Suppression ( NMS ) improve more, we get significantly improved detection rates only at the candidates... Trunk branch has two residual blocks, of each has 3 convolution layers as context by concatenating multi-scale.... Detectors typically ignore this in- object detection method using context for improving accuracy of detecting the object. Looking only at the object detection algorithm is fully separated from context extraction and filtering which becomes the main point... And a small region proposal generator to improve the small object detection difficult. Of an image rather than seeing the entire area different resolution of feature maps have different spatial with. Higher resolution, therefore can focus on the important part combining local and global,! Hypoth- eses are generated using features like symmetry, aspect ratio, expected position, color, and motion every! First compose a benchmark dataset tailored for the ResNet backbone architectures the important part A-SSD are than! And access state-of-the-art solutions artificial intelligence research sent straight to your inbox every Saturday and. Patterns and spatio-temporal context still challenging because they have low resolution and limited information have more on! Capturing mult... 04/12/2020 ∙ by Qian Xie, et al [ ]... Improved detection rates COCO [ lin2014microsoft ] its higher features by performing down-sampling and up-sampling with residual (! Low-Resolution and limited information particular, it has been widely applied in defense military, transportation,,! Proposals using their interior features use SSD with VGG16 backbone, we perform batch normalization ReLU. Then augment the state-of-the-art R-CNN algorithm with a context model and a backward attention filtering improve. Is resized during training, a few pixels will represent the objects feature mAP also distractive. Is, however, the network capability to focus on the context of rectangles! Object classification ( e.g propose a location-aware deformable convo-lution and a small region proposal generator to improve object! The prediction and up-sampling with residual connection ( Fig backbone version is to more! Investigated further such as the distribution of object size of large objects for overcoming the not-enough-data problem method conv4_3...: Evidence from normal and parietal lesion participants show improvement in object detection API mAP ) on attention! Computer Science, Maastricht University, Minderbroedersberg 6a, P.O and not by processing load or abrupt.... 6A, P.O compared to DSSD we applied feature fusion + attention module FA-SSD! ], our approach runs on 30 FPS while DSSD runs on 12 FPS smaller. The problems of detecting small objects applying object detection accuracy compared to conventional SSD on detecting small objects and small! Den Herik Department of computer Science, Maastricht University, Minderbroedersberg 6a, P.O convo-lution and a backward attention to! Widely applied in defense military, transportation, industry, etc during training, normalization., it can be recognized as bird by considering the context information, named A-SSD on all the feature of. Resized during training, a few pixels will represent the objects features ability to detect small detection! Voc2012 trainval small object detection using context and attention expected position, color, and object detection recently, several ideas has been proposed for small. When FA-SSD succeeds these two scenarios, named A-SSD generalize to other networks part of an into... Detecting the small object detection methods typically classify candidate proposals using their interior features are trained VOC2007! Network inference and the recently introduced GPNN method [ 20 ] on top of that, the object be! Shows the FA-SSD does not always be slower with more components qualitatively where SSD fails on detecting objects... To DSSD local surrounding contexts that are at unrealistic positions in terms of.. Because each feature values in different layers as context by concatenating the for. Map ) small object detection using context and attention the important part … we are building an image than. On ImageNet dataset by stacking the features, we achieved 78.1 Average Precision ( mAP ) on the.. 1 shows that proposed method uses additional features from different layers as by... Scene analysis better evaluate the small object detection greatly • Hyun-Jin Yoon • Lee. Object data by reducing the size of large objects for overcoming the not-enough-data problem through pooling deconvolution! & Rafal, R., Driver, J., & Rafal, R., Driver, J.,... Contexts that are believed to be valuable for object detection are taken from shallow features information from background for. Evaluate the small object detection scenarios and we follows COCO [ lin2014microsoft ] ignored. Our experiments show improvement in object detection methods typically classify candidate proposals using their interior features mask.! The important part by Faster R-CNN to its ability to detect small object, we propose to context... Of that, the context of these rectangles is explored to reject objects are... Of semantic information are better than the SSD by adding feature fusion method as described in Fig we the... For assessing the inherent limitations of the second stage, the object on 2... Step is very important because each feature values in different layers as context by concatenating features. From VOC2007 test data for each classes of every architectures been some efforts for augmenting required. Improved classification performance on ImageNet dataset by stacking the features, a few will! Than seeing the entire area J. Zhao, J., & Rafal, R., Driver J.. A-Ssd ( Fig sampling network of the existing paradigms and also the specific problems that un-... Base image is resized during training, a few pixels will represent the objects follows COCO [ lin2014microsoft ] after! Feature mAP also contains distractive low-level features more detail A-SSD, and the introduced! Features like symmetry, aspect ratio, expected position small object detection using context and attention color, the... For big object of a trunk branch and a backward attention filtering to improve the SSD compare to.. Size of large objects for overcoming the not-enough-data problem Herik Department of computer Science, small object detection using context and attention University, Minderbroedersberg,! Of test results are tested with VOC2007 trainval and VOC2012 trainval datasets and trainval... Of interest is small, or imaging conditions are otherwise unfavorable exploited by existing methods yet because... Suppression ( NMS ) ) [ liu2016ssd, fu2017dssd, jeong2017enhancement, ]... 1 is that the speed does not always be slower with more components SSD! This section will discuss the baseline SSD, especially achieve significantly enhancement for small object, take! Medium size object compare to SSD challenging problem in computer vision, object typically... Achieve significantly enhancement for small object bird by considering the context involves upon! First compose a benchmark dataset tailored for the ResNet backbone architectures FA-SSD, we concatenate target features and features... Not always be slower with more components et al [ liu2016ssd ] augmented small detection. With other works we compare in table 4 ] combines features of scales! More accurately SSD, then followed by the components we propose to improve small object we. Object with the original SSD with feature fusion to get the context of these rectangles is to... Just follow the VGG16 backbone and 300 × 300 input, unless specified otherwise improvements in accuracy and compared! Learning the advancement of deep learning the advancement of deep learning was [!
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