GCN-based Detection of Occluded Key Parts of Vehicle Target

被引：0
作者：
Wang, Yeru [1 ]
Yang, Geng [2 ]
Liu, Shu [3 ]
Xu, Xiao [4 ]
Chen, Huajie [4 ]
Qin, Feiwei [5 ]
Xu, Huajie [6 ]
机构：
[1] School of Cyberspace Security, Hangzhou Dianzi University, Zhejiang, Hangzhou
[2] Unit 32381 of PLA, Beijing
[3] Hangzhou Zhiyuan Research Institute Co., Ltd., Zhejiang, Hangzhou
[4] School of Automation, Hangzhou Dianzi University, Zhejiang, Hangzhou
[5] School of Computer Science and Technology, Hangzhou Dianzi University, Zhejiang, Hangzhou
[6] School of Information and Electronic Engineering, Zhejiang University of Science and Technology, Zhejiang, Hangzhou
来源：
Binggong Xuebao/Acta Armamentarii | 2024年 / 45卷
关键词：
graph convolutional neural network; object detection; occlusion; prior knowledge; rigid structure; vehicle key part;
D O I：
10.12382/bgxb.2024.0574
中图分类号：
学科分类号：
摘要：
The key parts of vehicle occluded due to complex backgrounds and variations in vehicle posture can not be accurately identified in images. A detection method based on partially deformable object graph convolutional network (PDO-GCN) is proposed for detecting the occluded key parts of vehicle. This method is founded on the rigid body structural relationships of vehicles, constructing a spatial association model between key parts on the 2D imaging plane based on PDO-GCN, and utilizes the detected results of visible key parts to estimate the locations of occluded ones. Experimental results demonstrate that the PDO-GCN model can effectively infer the complete vehicle structural information without the need for complex annotations, significantly improves the detection accuracy of occluded parts and fulfils the real-time requirements, thus showcasing considerable potential for practical application. © 2024 China Ordnance Industry Corporation. All rights reserved.
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页码：242 / 251
页数：9
相关论文
共 20 条
  • [1] GIRSHICK R., Fast R-CNN, Proceedings of the IEEE International Conference on Computer Vision, pp. 1440-1448, (2015)
  • [2] REN S Q, HE K M, GIRSHICK R, Et al., Faster R-CNN: towards real-time object detection with region proposal networks, Advances in Neural Information Processing Systems, 1, 28, pp. 91-99, (2015)
  • [3] CAI Z, VASCONCELOS N., Cascade R-CNN: delving into high quality object detection, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 6154-6162, (2018)
  • [4] LIU W, ANGUELOV D, ERHAN D, Et al., SSD: single shot multibox detector, Proceedings of the Computer Vision-ECCV 2016: 14th European Conference, pp. 21-37, (2016)
  • [5] LIN T Y, GOYAL P, GIRSHICK R, Et al., Focal loss for dense object detection, Proceedings of the IEEE International Conference on Computer Vision, pp. 2980-2988, (2017)
  • [6] BOCHKOVSKIY A, WANG C Y, LIAO H Y M., Yolov4: optimal speed and accuracy of object detection, (2020)
  • [7] TIAN Y L, LUO P, WANG X G, Et al., Deep learning strong parts for pedestrian detection, Proceedings of the 2015 IEEE International Conference on Computer Vision, pp. 1904-1912, (2015)
  • [8] ZHANG S F, WEN L Y, BIAN X, Et al., Occlusion-aware RCNN: detecting pedestrians in a crowd, Proceedings of the 15th European Conference on Computer Vision, pp. 657-674, (2018)
  • [9] ZHANG K, XIONG F, SUN P, Et al., Double anchor R-CNN for human detection in a crowd
  • [10] CHU X G, ZHENG A L, ZHANG X Y, Et al., Detection in crowded scenes: one proposal, multiple predictions, Proceedings of the 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 12211-12220, (2020)
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