Context-Aware Hierarchical Feature Attention Network For Multi-Scale Object Detection

2020 ◽  
Author(s):  
Xuelong Xu ◽  
Xiangfeng Luo ◽  
Liyan Ma
Keyword(s):  
Object Detection ◽  
Context Aware ◽  
Attention Network ◽  
Multi Scale ◽  
Feature Attention

scholarly journals Subtask Attention Based Object Detection in Remote Sensing Images

2021 ◽  
Vol 13 (10) ◽  
pp. 1925
Author(s):  
Shengzhou Xiong ◽  
Yihua Tan ◽  
Yansheng Li ◽  
Cai Wen ◽  
Pei Yan
Keyword(s):  
Remote Sensing ◽  
Object Detection ◽  
Feature Fusion ◽  
Detection Task ◽  
Feature Representation ◽  
Detection Accuracy ◽  
Remote Sensing Images ◽  
Attention Network ◽  
Multi Scale ◽  
Automatic Interpretation

Object detection in remote sensing images (RSIs) is one of the basic tasks in the field of remote sensing image automatic interpretation. In recent years, the deep object detection frameworks of natural scene images (NSIs) have been introduced into object detection on RSIs, and the detection performance has improved significantly because of the powerful feature representation. However, there are still many challenges concerning the particularities of remote sensing objects. One of the main challenges is the missed detection of small objects which have less than five percent of the pixels of the big objects. Generally, the existing algorithms choose to deal with this problem by multi-scale feature fusion based on a feature pyramid. However, the benefits of this strategy are limited, considering that the location of small objects in the feature map will disappear when the detection task is processed at the end of the network. In this study, we propose a subtask attention network (StAN), which handles the detection task directly on the shallow layer of the network. First, StAN contains one shared feature branch and two subtask attention branches of a semantic auxiliary subtask and a detection subtask based on the multi-task attention network (MTAN). Second, the detection branch uses only low-level features considering small objects. Third, the attention map guidance mechanism is put forward to optimize the network for keeping the identification ability. Fourth, the multi-dimensional sampling module (MdS), global multi-view channel weights (GMulW) and target-guided pixel attention (TPA) are designed for further improvement of the detection accuracy in complex scenes. The experimental results on the NWPU VHR-10 dataset and DOTA dataset demonstrated that the proposed algorithm achieved the SOTA performance, and the missed detection of small objects decreased. On the other hand, ablation experiments also proved the effects of MdS, GMulW and TPA.

scholarly journals Multi-Scale Dense Attention Network for Stereo Matching

Electronics ◽  
2020 ◽  
Vol 9 (11) ◽  
pp. 1881
Author(s):  
Yuhui Chang ◽  
Jiangtao Xu ◽  
Zhiyuan Gao
Keyword(s):  
Feature Extraction ◽  
Stereo Matching ◽  
State Of The Art ◽  
Ground Truth ◽  
Context Information ◽  
Context Aware ◽  
Feature Maps ◽  
Attention Network ◽  
Multi Scale ◽  
Benchmark Datasets

To improve the accuracy of stereo matching, the multi-scale dense attention network (MDA-Net) is proposed. The network introduces two novel modules in the feature extraction stage to achieve better exploit of context information: dual-path upsampling (DU) block and attention-guided context-aware pyramid feature extraction (ACPFE) block. The DU block is introduced to fuse different scale feature maps. It introduces sub-pixel convolution to compensate for the loss of information caused by the traditional interpolation upsampling method. The ACPFE block is proposed to extract multi-scale context information. Pyramid atrous convolution is adopted to exploit multi-scale features and the channel-attention is used to fuse the multi-scale features. The proposed network has been evaluated on several benchmark datasets. The three-pixel-error evaluated over all ground truth pixels is 2.10% on KITTI 2015 dataset. The experiment results prove that MDA-Net achieves state-of-the-art accuracy on KITTI 2012 and 2015 datasets.

Multi-scale Fusion with Context-aware Network for Object Detection

2018 ◽  
Author(s):  
Hanyuan Wang ◽  
Jie Xu ◽  
Linke Li ◽  
Ye Tian ◽  
Du Xu ◽  
...  
Keyword(s):  
Object Detection ◽  
Context Aware ◽  
Multi Scale

scholarly journals Context-aware Cross-level Fusion Network for Camouflaged Object Detection

2021 ◽  
Author(s):  
Yujia Sun ◽  
Geng Chen ◽  
Tao Zhou ◽  
Yi Zhang ◽  
Nian Liu
Keyword(s):  
Object Detection ◽  
State Of The Art ◽  
Context Aware ◽  
Global Context ◽  
Feature Representations ◽  
Multi Scale ◽  
Benchmark Datasets ◽  
Multi Level ◽  
High Level ◽  
Level Fusion

Camouflaged object detection (COD) is a challenging task due to the low boundary contrast between the object and its surroundings. In addition, the appearance of camouflaged objects varies significantly, e.g., object size and shape, aggravating the difficulties of accurate COD. In this paper, we propose a novel Context-aware Cross-level Fusion Network (C2F-Net) to address the challenging COD task. Specifically, we propose an Attention-induced Cross-level Fusion Module (ACFM) to integrate the multi-level features with informative attention coefficients. The fused features are then fed to the proposed Dual-branch Global Context Module (DGCM), which yields multi-scale feature representations for exploiting rich global context information. In C2F-Net, the two modules are conducted on high-level features using a cascaded manner. Extensive experiments on three widely used benchmark datasets demonstrate that our C2F-Net is an effective COD model and outperforms state-of-the-art models remarkably. Our code is publicly available at: https://github.com/thograce/C2FNet.

scholarly journals Automatic lumbar spinal MRI image segmentation with a multi-scale attention network

2021 ◽  
Author(s):  
Haixing Li ◽  
Haibo Luo ◽  
Wang Huan ◽  
Zelin Shi ◽  
Chongnan Yan ◽  
...  
Keyword(s):  
Image Segmentation ◽  
Attention Network ◽  
Multi Scale ◽  
Spinal Mri ◽  
Lumbar Spinal ◽  
Mri Image

scholarly journals A new multi-scale backbone network for object detection based on asymmetric convolutions

2021 ◽  
Vol 104 (2) ◽  
pp. 003685042110113
Author(s):  
Xianghua Ma ◽  
Zhenkun Yang
Keyword(s):  
Object Detection ◽  
Image Features ◽  
Detection Accuracy ◽  
Mobile Platforms ◽  
Multi Scale ◽  
Backbone Network ◽  
Aspect Ratios ◽  
Pascal Voc ◽  
Scale Characteristics ◽  
Detection Speed

Real-time object detection on mobile platforms is a crucial but challenging computer vision task. However, it is widely recognized that although the lightweight object detectors have a high detection speed, the detection accuracy is relatively low. In order to improve detecting accuracy, it is beneficial to extract complete multi-scale image features in visual cognitive tasks. Asymmetric convolutions have a useful quality, that is, they have different aspect ratios, which can be used to exact image features of objects, especially objects with multi-scale characteristics. In this paper, we exploit three different asymmetric convolutions in parallel and propose a new multi-scale asymmetric convolution unit, namely MAC block to enhance multi-scale representation ability of CNNs. In addition, MAC block can adaptively merge the features with different scales by allocating learnable weighted parameters to three different asymmetric convolution branches. The proposed MAC blocks can be inserted into the state-of-the-art backbone such as ResNet-50 to form a new multi-scale backbone network of object detectors. To evaluate the performance of MAC block, we conduct experiments on CIFAR-100, PASCAL VOC 2007, PASCAL VOC 2012 and MS COCO 2014 datasets. Experimental results show that the detection precision can be greatly improved while a fast detection speed is guaranteed as well.

Multi-scale object detection for high-speed railway clearance intrusion

2021 ◽  
Author(s):  
Runliang Tian ◽  
Hongmei Shi ◽  
Baoqing Guo ◽  
Liqiang Zhu
Keyword(s):  
Object Detection ◽  
High Speed ◽  
High Speed Railway ◽  
Multi Scale
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