Perception Technology and Application of Complex Urban Traffic Environment Based on Target Detection

doi:10.12068/j.issn.1005-3026.2025.20230297

Abstract

Abstract:

Machine vision-based environmental perception technology is one of the key tasks in the field of intelligent transportation. Traditional deep learning algorithms typically meet the detection needs of individual targets in simple scenarios. However， they are not capable of addressing the intelligent perception requirements in complex traffic environment. To improve the intelligent perception capability of vehicles in such environment， this paper proposes an improved YOLOv8 object detection network model， integrating attention mechanisms， optimizers， and deformable convolutional layers to achieve multi-target detection in complex urban traffic environment. To verify the effectiveness of the algorithm， comparative experiment were conducted using YOLOv4， YOLOv8， and the improved YOLOv8 algorithm on sample images from complex traffic environments. The results show that， compared to YOLOv4 and YOLOv8， the improved YOLOv8 algorithm increased the average accuracy by 40.76% and 16.92%， respectively. The detection accuracy and real-time performance of the improved YOLOv8 algorithm meet the practical application requirements， and through multi-sensor information fusion， it can realize intelligent perception in complex urban traffic environment.

Key words: YOLOv8, target detection, complex urban traffic, environment perception, intelligent transportation

CLC Number:

P 23

Aisan XIERAILI, De-fu CHE, Duo WANG, Tian YU. Perception Technology and Application of Complex Urban Traffic Environment Based on Target Detection[J]. Journal of Northeastern University(Natural Science), 2025, 46(5): 29-36.

Figures/Tables 13

References 27

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训练参数	数值	训练参数	数值
epoch	100	imgsize	640
batch	16	workers	4
optimizer	Adam	patience	50
save_period	10	close_mosaic	10

训练参数	数值	训练参数	数值
epoch	100	imgsize	640
batch	16	workers	4
optimizer	Adam	patience	50
save_period	10	close_mosaic	10

模型	主干网络	输入尺寸/像素	mAP/%	FPS	IoU
YOLOv4	CSPDarknet53	416×416	44.19	47	0.5
YOLOv8	CSPDarknet+C2F	640×640	53.20	87	0.5
改进YOLOv8	CSPDarknet+C2F	640×640	62.20	78	0.5

模型	主干网络	输入尺寸/像素	mAP/%	FPS	IoU
YOLOv4	CSPDarknet53	416×416	44.19	47	0.5
YOLOv8	CSPDarknet+C2F	640×640	53.20	87	0.5
改进YOLOv8	CSPDarknet+C2F	640×640	62.20	78	0.5

目标类型	模型平均精度mAP50			改进模型精度提升量
目标类型	YOLOv4	YOLOv8	改进YOLOv8	相对YOLOv4	相对YOLOv8
总平均	44.19	53.20	62.20	18.01	9.00
交通标志牌	55.59	68.40	68.30	12.71	-0.10
路面破损	57.60	73.60	81.10	23.50	7.50
井盖	50.95	47.50	51.10	0.15	3.60
井盖丢失	96.62	86.50	97.40	0.78	10.90
垃圾桶	53.95	71.80	75.80	21.85	4.00
垃圾桶倾倒	56.93	95.00	99.50	42.57	4.50
雨水立箅	53.26	58.80	67.40	14.14	8.60
雨水立箅破损	00.00	33.20	99.50	99.50	66.30
火焰	45.07	63.10	65.40	20.33	2.30
违规摆摊	44.19	47.00	54.70	10.51	7.70