Journal of Jilin University(Engineering and Technology Edition) ›› 2024, Vol. 54 ›› Issue (9): 2658-2667.doi: 10.13229/j.cnki.jdxbgxb.20221461

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Road object detection method based on improved YOLOv5 algorithm

Hong-zhi WANG(),Ming-xuan SONG,Chao CHENG,Dong-xuan XIE()   

  1. College of Computer Science and Engineering，Changchun University of Technology，Changchun 130012，China
  • Received:2022-11-16 Online:2024-09-01 Published:2024-10-29
  • Contact: Dong-xuan XIE E-mail:wanghongzi@ccut.edu.cn;xiedongxuan@ccut.edu.cn

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Abstract:

Aiming at the object detection problems of the existing network such as poor remote object recognition effect， insufficient object feature expression and inaccurate object positioning， a road object detection method based on the improved YOLOv5 algorithm was proposed. Firstly， the feature extraction structure of YOLOv5 algorithm was summarized， and the shortcomings of the original network structure were analyzed. Secondly， the small object detection layer was added to the original network， and the recognition ability of the network for distant objects was improved by supplementing the fusion feature layer and introducing additional detection heads. Thirdly， the original detection head was decoupled， and the expression ability of the network to object features was improved by changing the border regression and object classification process to two branches. Then， the prior box was reclustered， and the height to width ratio of the prior box was adjusted by the K-means++ algorithm to enhance the network's ability to locate the object. Finally， AP， mAP and FPS were used as evaluation indicators for ablation， comparison and visual verification experiments. The results show that the detection speed of the proposed algorithm on the BDD100K dataset is 95.2 frames per second， and the average accuracy reaches 55.6%， which is 6.7% higher than that of the YOLOv5 algorithm. It can be seen that the improved YOLOv5 algorithm not only meets the requirements of real-time detection， but also has good object detection accuracy， which is suitable for road object detection tasks in complex traffic environments， and has guiding significance for improving the visual perception ability of autonomous vehicles.

Key words: transporstation safety engineering, situational awareness, object detection, YOLOv5, multi-scale detection

CLC Number: 

  • U492.8

Fig.1

Structural diagram of YOLOv5 network"

Fig.2

Schematic diagram of C3 module"

Fig.3

Schematic diagram of SPPF module"

Fig.4

Schematic diagram of PANet fusion structure"

Fig.5

Schematic diagram of detection process"

Fig.6

Schematic diagram of decoding process"

Fig.7

Structural diagram of improved YOLOv5 network"

Fig.8

Schematic diagram of decoupling detection head"

Table 1

Priori box frame before and after reclustering"

检测层聚类前聚类后
160×160None［6，12，8，29，14，16］
80×80［10，13，16，30，33，23］［15，61，19，31，33，129］
40×40［30，61，62，45，59，119］［34，32，38，55，65，76］
20×20［116，90，156，198，373，326］［98，124，137，204，213，328］

Fig.9

Different road scenarios in the BDD100K dataset"

Table 2

The dataset used for the experiment"

类别名称训练数量测试数量
Person （行人）11 8461 419
Rider （骑手）58168
Car （小汽车）92 01410 524
Bus （公共汽车）1 402195
Truck （货车）3 796451
Bike （自行车）885122
Motor （摩托车）40943
Traffic light （交通灯）24 2182 672
Traffic sign （交通标志）31 3733 541

Table 3

Ablation experiment results"

状态网络结构mAP@0.5/%mAP@0.5∶0.95/%
1YOLOv548.925.9
2YOLOv5+小目标检测层52.927.1
3YOLOv5+小目标检测层+检测头解耦54.428.3
4YOLOv5+小目标检测层+检测头解耦+先验框重聚类55.628.9

Table 4

Performance of this algorithm is compared with various object detection algorithms in recent years"

算法AP/%mAP@0.5/%FPS
PersonRiderCarBusTruckBikeMotorTraffic lightTraffic sign
Faster R-CNN［23］30.623.956.747.549.232.328.48.921.633.215.3
SSD［16］31.117.659.544.047.130.627.522.126.234.068.8
YOLOv5［18］52.631.572.448.352.238.635.853.055.948.9175.4
YOLOv4-tiny［24］18.52.944.026.531.016.75.810.624.420.294.5
YOLOX［25］55.328.774.353.256.340.042.055.858.051.546.7
YOLOv7［26］66.345.976.451.953.748.846.156.860.756.333.2
YOLOv7-tiny［26］49.234.069.544.948.541.029.141.345.544.863.4
本文60.838.478.349.956.845.442.564.164.455.695.2

Fig.10

Comparison of road object detection results in sunny scenarios"

Fig.11

Comparison of road object detection results in rainy scenarios"

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