改进YOLOv4算法的复杂视觉场景行人检测方法

doi:10.11959/j.issn.1000-0801.2021198

Abstract

Abstract:

At present, the difficulty of pedestrian detection has been dramatically increased because of some problems, such as the dark or exposed illumination, bad weather, serious occlusion, large difference size of pedestrians and blurred images in complex visual scenes.Therefore, an improved YOLOv4 algorithm was proposed, which improved the detection performance of pedestrian detection in complex visual scenes, aiming at the problems of low accuracy and highly missed detection rate.Firstly, the self-annotation data set pedetrian were constructed.Secondly, the hybrid dilated convolution (HDC) was added into the backbone network to improve the ability of pedestrian feature extraction.Finally, in order to obtain more detailed feature, the spatial jagged dilated convolution (SJDC) structure was proposed to replace the spatial pyramid pooling structure.The experimental results show that the average precision (AP) of the proposed algorithm can achieve 90.08%.The proposed algorithm can substantially improve AP by 7.2%, and the log-average miss rate (LAMR) reduce by 13.69% compared with the original YOLOv4 algorithm.

Key words: complex visual scenes, YOLOv4, hybrid dilated convolution, spatial jagged dilated convolution

CLC Number:

TP393

Shuai KANG, Jianwu ZHANG, Zunjie ZHU, Guofeng TONG. An improved YOLOv4 algorithm for pedestrian detection in complex visual scenes[J]. Telecommunications Science, 2021, 37(8): 46-56.

Figures/Tables 19

References 28

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阈值	YOLOv3	YOLOv4	YOLOv5
0.5	79.16%	81.46%	83.53%
0.6	67.89%	71.55%	74.05%
0.7	44.93%	49.37%	52.35%
0.8	18.36%	20.62%	23.06%

模型	Epoch	LAMR	AP_person
YOLOv3	150	42.42%	79.16%
YOLOv4	150	40.03%	82.88%
SJDC-YOLOv4	150	30.33%	86.90%

模型	LAMR	AP_person
YOLOv3	42.42%	79.16%
YOLOv4	40.03%	82.88%
参考文献[14]改进的YOLOv4	36.80%	84.00%
本文改进的YOLOv4	26.34%	90.08%

模型	LAMR	AP_person
YOLOv3	27.17%	86.36%
YOLOv4	8.63%	95.89%
参考文献[14]改进的YOLOv4	7.07%	96.22%
本文改进的YOLOv4	5.70%	97.00%

An improved YOLOv4 algorithm for pedestrian detection in complex visual scenes

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