GCN-based Detection of Occluded Key Parts of Vehicle Target

doi:10.12382/bgxb.2024.0574

Abstract

Abstract:

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.

Key words: object detection, vehicle key part, occlusion, rigid structure, prior knowledge, graph convolutional neural network

CLC Number:

TP301

WANG Yeru, YANG Geng, LIU Shu, XU Xiao, CHEN Huajie, QIN Feiwei, XU Huajie. GCN-based Detection of Occluded Key Parts of Vehicle Target[J]. Acta Armamentarii, 2024, 45(S1): 242-251.

Figures/Tables 18

Fig.1 Distribution of 14 key parts of tanks

Fig.2 Overall plan for detecting the key parts of vehicles

Fig.3 GCN network structure corresponding to the key parts of vehicle

Fig.4 PDO-GCN structure

Fig.5 Anti-confusion feature encoding module

Fig.6 Graph convolution module based on anti-confusion feature encoding

Fig.7 Schematic diagram of vehicle spatial coordinates

Fig.8 Scene setting diagram

Fig.9 Schematic diagram of external camera parameters and vehicle deformation parameters

Fig.10 Dataset 1-Examples of tank models captured by drones

Fig.11 Dataset 2-Examples of near tank data

Table 1 Ablation experimental results (Dataset 1)

模块			召回率/%	精确率/%	FPS
YOLOv8	PDO-GCN	Anti Confusion	召回率/%	精确率/%	FPS
√			74.87	81.53	61.3
√	√		13.15	14.23	57.2
√	√	√	84.23	83.52	57.2

Table 2 Ablation experimental results (Dataset 2)

	YOLOv8	PDO-GCN	Anti Confusion	召回率/%	精确率/%	FPS
A	√			77.98	78.35	60.6
B	√	√		12.62	15.56	55.3
C	√	√	√	81.95	82.33	55.1

Fig.12 Detection results of PDO-GCN on different datasets

Table 3 Detection results of various algorithms (Dataset 1)

算法	召回率/%	精确率/%	FPS
CrowdDet	45.94	75.63	21.2
DeepVoting	47.01	65.67	19.7
YOLOv8	74.87	81.53	61.3
YOLOv8+PDO-GCN	84.23	83.52	57.2

Table 4 Detection results of various algorithms (Dataset 2)

算法	召回率/%	精确率/%	FPS
CrowdDet	78.63	71.37	19.7
DeepVoting	79.58	78.12	17.1
YOLOv8	77.98	78.35	60.6
YOLOv8+PDO-GCN	81.95	82.33	55.1

Fig.13 Partial experimental results on Dataset 1

Fig.14 Partial experimental results on Dataset 2

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