基于改进YOLOv5的路面坑洼检测方法

doi:10.16180/j.cnki.issn1007-7820.2024.07.007

Abstract

Abstract:

Pothole is a common road disease, it reduce driving safety, accurate and rapid detection of potholes is more important.In viewof the problem that the detection accuracy of existing pothole detection methods is not high in the scenario of small targets and dense targets, an improved YOLOv5(You Only Look Once version 5) model is proposed in this study.TheCBAM(Convolutional Block Attention Module) is introduced into YOLOv5's backbone network to improve the model's ability to pay attention to key features. The loss function of YOLOv5 is changed to EIoU(Efficient Intersection over Union) to improve the detection accuracy of the model.The experimental results show that the proposed model can detect Potholes quickly and accurately in the scenarios of small targets and dense targets, and the mAP(mean Average Precision) in the open source Annotated Potholes Image Dataset reaches 82%. Compared with YOLOv5 and other mainstream methods, it is also improved.

Key words: pavement potholes, deep learning, YOLOv5, attention mechanism, CBAM attention, small target detection, dense target detection, loss function

CLC Number:

TP391

HE Xing, HUANG Yongming, ZHU Yong. Pavement Pothole Detection Method Based on Improved YOLOv5[J].Electronic Science and Technology, 2024, 37(7): 53-59.

Figures/Tables 13

Figure 1.

Figure 2.

Figure 3.

Figure 4.

Figure 5.

Table 1.

Figure 6.

Table 2.

Table 3.

Figure 7.

Table 4.

Figure 8.

Table 5.

References 22

[1]	罗晖, 余俊英, 涂所成. 基于深度学习的公路路面病害检测算法[J]. 科学技术与工程, 2022, 22(13):5299-5305.
	Luo Hui, Yu Junying, Tu Suocheng. Pavement defect detection algorithm based on deep learning[J]. Science Technology andEngineering, 2022, 22(13):5299-5305.
[2]	李良福, 孙瑞赟. 复杂背景下基于图像处理的桥梁裂缝检测算法[J]. 激光与光电子学进展, 2019, 56(6):112-122.
	Li Liangfu, Sun Ruiyun. Bridge crack detection algorithm based on image processing under complex background[J]. Laser and Optoelectronics Progress, 2019, 56(6):112-122.
[3]	王玲敏, 段军, 辛立伟. 引入注意力机制的YOLOv5安全帽佩戴检测方法[J]. 计算机工程与应用, 2022, 58(9):303-312. doi: 10.3778/j.issn.1002-8331.2112-0242
	Wang Lingmin, Duan Jun, Xin Liwei. YOLOv5 helmet wear detection method with introduction of attention mechanism[J]. Computer Engineering and Applications, 2022, 58(9):303-312. doi: 10.3778/j.issn.1002-8331.2112-0242
[4]	程长文, 陈玮, 陈劲宏, 等. 改进YOLO的口罩佩戴实时检测方法[J]. 电子科技, 2023, 36(2):73-80.
	Cheng Changwen, Chen Wei, Chen Jinhong, et al. YOLO-improve detection method of real-time mask wearing[J]. Electronic Science and Technology, 2023, 36(2):73-80.
[5]	Suong L K, Jangwoo K. Detection of potholes using a deep convolutional neural network[J]. Journal of Universal Computer Science, 2018, 24(9):1244-1257.
[6]	罗晖, 贾晨, 李健. 基于改进YOLOv4的公路路面病害检测算法[J]. 激光与光电子学进展, 2021, 58(14):336-344.
	Luo Hui, Jia Chen, Li Jian. Road surface disease detection algorithm based on improved YOLOv4[J]. Laser and Optoelectronics Progress, 2021, 58(14):336-344.
[7]	Wang W, Wu B, Yang S, et al. Road damage detection and classification with faster R-CNN[C]. Seattle: IEEE International Conference on Big Data, 2018:5220-5223.
[8]	张宁. 基于Faster R-CNN的公路路面病害检测算法的研究[D]. 南昌: 华东交通大学, 2019:59-63.
	Zhang Ning. Study on detection algorithm for road surface disease based on Faster R-CNN[D]. Nanchang: East China Jiaotong University, 2019:59-63.
[9]	黄璐, 毛晓艳, 杜航, 等. 基于深度学习网络的星表非结构化岩石目标辨识方法研究[J]. 空间控制技术与应用, 2021, 47(6):27-33.
	Huang Lu, Mao Xiaoyan, Du Hang, et al. Onstar catalog unstructured rock target identification method based on deep learning network[J]. Aerospace Control and Application, 2021, 47(6):27-33.
[10]	Zhu X, Lyu S, Wang X, et al. TPH-YOLOv5:Improved YOLOv5 based on transformer prediction head for object detection on drone-captured scenarios[C]. Montreal: Proceedings of the IEEE/CVF International Conference on Computer Vision, 2021:2778-2788.
[11]	Nepal U, Eslamiat H. Comparing YOLOv3,YOLOv4 and YOLOv5 for autonomous landing spot detection in faulty UAVs[J]. Sensors, 2022, 22(2):464-478.
[12]	许德刚, 王露, 李凡. 深度学习的典型目标检测算法研究综述[J]. 计算机工程与应用, 2021, 57(8):10-25. doi: 10.3778/j.issn.1002-8331.2012-0449
	Xu Degang, Wang Lu, Li Fan. Review of typical object detection algorithms for deep learning[J]. Computer Engineering and Applications, 2021, 57(8):10-25. doi: 10.3778/j.issn.1002-8331.2012-0449
[13]	Jia W, Xu S, Liang Z, et al. Real-time automatic helmet detection of motorcyclists in urban traffic using improved YOLOv5 detector[J]. IET Image Processing, 2021, 15(14):3623-3637.
[14]	Li Z Z. Road aerial object detection based on improved YOLOv5[C]. Beihai: International Conference on Computer, Big Data and Artificial Intelligence, 2022: 1-5.
[15]	冯凯, 张书雅, 李锦暄, 等. 基于卷积神经网络的返回舱识别[J]. 现代信息科技, 2021, 5(10):20-26.
	Feng Kai, Zhang Shuya, Li Jinxuan, et al. Identification ofreturn cabin based on convolutional neural network[J]. Modern Information Technology, 2021, 5(10):20-26.
[16]	张伟, 刘娜, 江洋, 等. 基于YOLO神经网络的垃圾检测与分类[J]. 电子科技, 2022, 35(10):45-50.
	Zhang Wei, Liu Na, Jiang Yang, et al. Garbage detection and classification based on YOLO neural network[J]. Electronic Science and Technology, 2022, 35(10):45-50.
[17]	Woo S, Park J, Lee J Y, et al. Cbam:Convolutional block attention module[C]. Munich: Proceedings of the European Conference on Computer Vision, 2018:3-19.
[18]	Zhang Y F, Ren W, Zhang Z, et al. Focal and efficient IOU loss for accurate bounding box regression[J]. Neurocomputing, 2022, 50(6):146-157.
[19]	马志强. 基于改进YOLOv5的海珍品目标检测算法[J]. 现代信息科技, 2021, 5(18):80-85.
	Ma Zhiqiang. Sea treasure target detection algorithm based on improved YOLOv5[J]. Modern Information Technology, 2021, 5(18):80-85.
[20]	Park S S, Tran V T, Lee D E. Application of various yolo models for computer vision-based real-time pothole detection[J]. Applied Sciences, 2021, 11(23):11217-11229.
[21]	Bučko B, Lieskovská E, Zábovská K, et al. Computer vision based pothole detection under challenging conditions[J]. Sensors, 2022, 22(22):8871-8878.
[22]	Heo D H, Choi J Y, Kim S B, et al. Image-based pothole detection using multiscale feature network and risk assessment[J]. Electronics, 2023, 12(4):810-826.

序号	输入来源	模块大小	模块名称	模块参数
0	-1	3 520	Focus	[3,32,3]
1	-1	19 170	CBAM	[32,64,3,2]
2	-1	18 816	C3	[64,64,1]
3	-1	76 130	CBAM	[64,128,3,2]
4	-1	156 928	C3	[128,128,3]
5	-1	303 714	CBAM	[128,256,3,2]
6	-1	625 152	C3	[256,256,3]
7	-1	1 180 672	Conv	[256,512,3,2]
8	-1	656 896	SPP	[512,512,[5,9,13]]
9	-1	1 182 720	C3	[512,512,1,False]
10	-1	131 584	Conv	[512,256,1,1]
11	-1	0	Upsample	[None,2,’nearest’]
12	[-1,6]	0	Concat	[1]
13	-1	361 984	C3	[512,256,1,False]
14	-1	33 024	Conv	[256,128,1,1]
15	-1	0	Upsample	[None,2,’nearest’]
16	[-1,4]	0	Concat	[1]
17	-1	90 880	C3	[256,128,1,False]
18	-1	147 712	Conv	[128,128,3,2]
19	[-1,14]	0	Concat	[1]
20	-1	296 448	C3	[256,256,1,False]
21	-1	590 336	Conv	[256,256,3,2]
22	[-1,10]	0	Concat	[1]
23	-1	1 182 720	C3	[512,512,1,False]

实验环境	配置
操作系统	Linux Ubuntu 18.04
处理器	Intel(R) Xeon(R) CPU E5-2678 v3 @2.50 GHz
内存	8 GB
显卡	NVIDIA Tesla K80(12 GB)
显卡加速	cuda11.1 cuDNN 8.0.5
开发环境	Python 3.7
深度学习框架	Pytorch 1.8.1

参数名	参数值
LEANING_RATE(学习率)	0.010 0
MOMENTUM(冲量)	0.937 0
WEIGHT_DECAY(权重衰减)	0.000 5
BATCH_SIZE(批大小)	16
EPOCH(迭代次数)	100

对比模型	mAP/%	帧速率/frame·s^-1
Sparse R-CNN	72.6	-
YOLOv3	77.1	28.57
YOLOv3-SPP	79.1	27.78
YOLOv4	77.7	25.48
YOLOv4-tiny	78.7	31.76
SPFPN-YOLOv4-tiny	79.6	38.00
YOLOv5s	75.3	44.44
YOLOv5m	73.3	23.58
改进YOLOv5	82.0	35.71

对比模型	YOLOv5	YOLOv5+ CBAM	YOLOv5+ EIoU	YOLOv5+ CBAM+EIoU
精确率/%	76.40	79.60	77.70	77.50
召回率/%	72.50	75.30	73.60	76.60
F1分数/%	75.00	77.00	76.00	77.00
mAP/%	75.30	80.10	77.60	82.00
帧速率 /frame·s^-1	44.44	35.84	43.48	35.71

Pavement Pothole Detection Method Based on Improved YOLOv5

RichHTML

PDF (PC)

Like

Knowledge

Abstract

Cite this article

share this article

Figures/Tables 13

References 22

Related Articles 15

Metrics

Comments

Recommended 0

[1]	LI Jun, LIU Hu, YANG Haima, WANG Yuan, XU Wencheng, HUANG Hongxin. Research on AGV Path Fusion of Object Detection and DWA Algorithm [J]. Electronic Science and Technology, 2024, 37(7): 33-42.
[2]	WANG Xinyu, ZHAO Jingwen, LIU Xiang, SHI Yunyu, SHE Yunlang. YOLOv3 Lung Nodule Detection Based on Coordinate Attention [J]. Electronic Science and Technology, 2024, 37(6): 1-7.
[3]	YE Yuxin, JU Zhiyong, LAI Ying. Traffic Sign Detection Algorithm Incorporating Receptive Field Enhancement Module and Attention Mechanism [J]. Electronic Science and Technology, 2024, 37(6): 8-16.
[4]	YUAN Ao, QI Jinpeng, JIA Can, XUE Yuxin, GUO Yangyang. A Classification Method of Time Series Pathological Data Segments Based on Deep Learning [J]. Electronic Science and Technology, 2024, 37(6): 84-91.
[5]	ZHU Zihao, SONG Yan. Lightweight Capsule Network Fusing Attention and Capsule Pooling [J]. Electronic Science and Technology, 2024, 37(5): 1-8.
[6]	XIA Rongcheng, LIU Deer. Vehicle Detection and Analysis in Urban Waterlogging Area Based on Deep Learning [J]. Electronic Science and Technology, 2024, 37(5): 18-24.
[7]	MA Wenjie, ZHANG Xuanxiong. Research on Blind Roads and Obstacle Recognition Algorithm Based on Deep Learning [J]. Electronic Science and Technology, 2024, 37(3): 75-83.
[8]	WANG Xiaozhu,YU Lianzhi. Small Object Detection Based on Convolution and Self-Attention of Aggregation [J]. Electronic Science and Technology, 2024, 37(2): 14-22.
[9]	YU Zhihong,LI Feifei. Semi-Supervised Medical Image Segmentation Method Based on Meta-Learning and Neural Architecture Search [J]. Electronic Science and Technology, 2024, 37(1): 17-23.
[10]	HUANG Zixuan,LI Qiaoxing. Research on Identification of Urban Illegal Vehicles Based on Random Forest Model [J]. Electronic Science and Technology, 2024, 37(1): 66-71.
[11]	LI Zenghui,WANG Wei. Research Progress of Medical Image Segmentation Method Based on Deep Learning [J]. Electronic Science and Technology, 2024, 37(1): 72-80.
[12]	WANG Jiawei,YU Xiao. Review of Text Classification Research Based on Deep Learning [J]. Electronic Science and Technology, 2024, 37(1): 81-86.
[13]	Bin ,WANG Sen. Visual Detection of Structural Cracks Using Depth Deformable Contour ModelLAI [J]. Electronic Science and Technology, 2023, 36(9): 35-40.
[14]	SUN Xi,YU Lianzhi. Image Dehazing Algorithm Based on Residual Attention and Semi-Supervised Learning [J]. Electronic Science and Technology, 2023, 36(9): 50-57.
[15]	DANG Xiaofang,CAI Xingyu. Transformer-Based Maneuvering Target Tracking [J]. Electronic Science and Technology, 2023, 36(9): 86-92.