基于改进YOLOv7-tiny的道路病害检测算法

doi:10.11996/JG.j.2095-302X.2024050987

图学学报 ›› 2024, Vol. 45 ›› Issue (5): 987-997.DOI: 10.11996/JG.j.2095-302X.2024050987

• 图像处理与计算机视觉 • 上一篇下一篇

基于改进YOLOv7-tiny的道路病害检测算法

谢国波(), 林松泽, 林志毅(), 吴陈锋, 梁立辉

广东工业大学计算机学院，广东广州 510006

收稿日期:2024-05-21 修回日期:2024-08-28 出版日期:2024-10-31 发布日期:2024-10-31
通讯作者:林志毅(1979-)，男，讲师，博士。主要研究方向为人工智能和生物信息学等。E-mail：lzy291@gdut.edu.cn
第一作者:谢国波(1977-)，男，教授，博士。主要研究方向为计算智能及其在遥感影像处理应用、高光谱遥感和复杂疾病模式挖掘等。E-mail：xiegb@gdut.edu.cn
基金资助:
国家自然科学基金项目(61802072)

Road defect detection algorithm based on improved YOLOv7-tiny

XIE Guobo(), LIN Songze, LIN Zhiyi(), WU Chenfeng, LIANG Lihui

School of Computer Science, Guangdong University of Technology, Guangzhou Guangdong 510006, China

Received:2024-05-21 Revised:2024-08-28 Published:2024-10-31 Online:2024-10-31
Contact: LIN Zhiyi (1979-), lecturer, Ph.D. His main research interests cover artificial intelligence and bioinformatics, etc. E-mail：lzy291@gdut.edu.cn
First author：XIE Guobo (1977-), professor, Ph.D. His main research interests cover computational intelligence and its application to remote sensing image processing, hyperspectral remote sensing andcomplex disease pattern mining, etc. E-mail：xiegb@gdut.edu.cn
Supported by:
National Natural Science Foundation of China(61802072)

摘要/Abstract

摘要：

针对目前道路病害检测方法参数量较大、小目标病害检测效果差且易出现误检、漏检的问题，提出一种基于改进YOLOv7-tiny的道路病害检测算法。引入深度可分离卷积(DSC)和无参注意力机制(SimAM)设计ELAN-SimAM-D结构，减少计算量和参数量以实现轻量化，同时加强模型的特征提取和特征融合的能力；引入自适应指数加权池化和自适应融合设计SPPAda结构作为空间金字塔池化结构，增强道路病害信息的保留程度，降低病害的漏检；新增P2小目标网络层，加强对较小目标病害的检测能力，提高模型的检测精度；设计新的损失函数NWD-EIOU替换原CIOU损失函数，提高小目标定位的精度。实验结果表明，相较于原始的YOLOv7-tiny算法，改进后的YOLOv7-tiny算法在自建实验数据集下mAP@0.5达到83.14%，提升了3.50%，召回率上提升了4.96%，模型的参数量降低了33.84%，能够满足道路病害检测的需求。

关键词: YOLOv7-tiny, 道路病害检测, 自适应指数加权池化, SimAM注意力机制, SPPAda结构, P2小目标网络层

Abstract:

To address the current issues in road damage detection methods, such as large parameter sizes, poor performance in detecting small targets, and high rates of false positives and missed detections, an improved YOLOv7-tiny-based road defect detection algorithm was proposed. The ELAN-SimAM-D structure was designed by introducing depthwise separable convolution (DSC) and a parameter-free attention mechanism, which could reduce computational and parameter sizes to achieve a lightweight model while enhancing the model’s feature extraction and fusion capabilities. The SPPAda structure, which incorporated adaptive exponential pooling and adaptive fusion, was introduced as a spatial pyramid pooling structure to enhance the retention of road defect information and improve detection accuracy. A new P2 small target network layer was added to strengthen the detection capability for smaller target defects, improving detection accuracy. A new loss function, NWD-EIOU, was designed to replace the original CIOU loss function, improving the localization accuracy for small targets. Experimental results showed that compared to the original YOLOv7-tiny algorithm, the improved YOLOv7-tiny algorithm achieved an mAP@0.5 of 83.14% on a self-built experimental dataset, an increase of 3.50%, with a 4.96% improvement in recall rate, and a 33.84% reduction in the model’s parameter size, meeting the requirements for road defect detection.

Key words: YOLOv7-tiny, road defect detection, adaptive exponential pooling, SimAM attention mechanism, SPPAda structure, P2 small target network layer

中图分类号:

TP391

谢国波, 林松泽, 林志毅, 吴陈锋, 梁立辉. 基于改进YOLOv7-tiny的道路病害检测算法[J]. 图学学报, 2024, 45(5): 987-997.

XIE Guobo, LIN Songze, LIN Zhiyi, WU Chenfeng, LIANG Lihui. Road defect detection algorithm based on improved YOLOv7-tiny[J]. Journal of Graphics, 2024, 45(5): 987-997.

图/表 14

图1 YOLOv7-tiny算法网络结构

Fig. 1 YOLOv7-tiny algorithm network structure

图2 标准卷积结构图

Fig. 2 The structure diagram of standard convolution

图3 深度可分离卷积结构图

Fig. 3 The structure diagram of depthwise separable convolution

图4 SimAM注意力机制

Fig. 4 SimAM attention mechanism

图5 ELAM-SimAM-D结构图((a) ELAN结构；(b) ELAN-SimAM-D结构)

Fig. 5 ELAM-SimAM-D structure ((a) ELAN structure; (b) ELAN-SIMAM-D structure)

图6 不同池化方法对比((a)平均池化；(b)最大池化；(c)平滑近似最大池化；(d)平滑近似平均池化；(e)自适应指数加权池化)

Fig. 6 Comparison of different pooling methods ((a) Average pooling; (b) Max pooling; (c) Exponential maximum pooling; (d) Exponential dice-s?rensen coefficient weighting pooling; (e) Adaptive exponential pooling)

图7 AdaFusion结构图

Fig. 7 AdaFusion structure diagram

图8 结构图对比((a) SPPCSPC结构；(b) SPPAda结构)

Fig. 8 Comparison of structural diagrams ((a) SPPCSPC structure; (b) SPPAda structure)

图9 改进YOLOv7-tiny算法网络结构

Fig. 9 Improved YOLOv7-tiny algorithm network structure

图10 滑动窗口裁剪算法

Fig. 10 Sliding window cropping algorithm

表1 消融实验结果对比

Table 1 Comparison of ablation experiment results

组别	模型	P/%	R%	mAP@0.5/%	Params/M	FPS
1	YOLOV7-tiny	73.04	78.74	79.64	11.70	72.23
2	YOLOV7-tiny+A	73.82	79.71	81.22	7.47	64.24
3	YOLOV7-tiny+A+B	74.12	81.52	81.79	7.60	63.21
4	YOLOV7-tiny+A+B+C	75.42	82.81	82.74	7.74	60.41
5	YOLOV7-tiny+A+B+C+D	75.62	83.70	83.14	7.74	60.41

表2 对比实验结果对比

Table 2 Comparison of experimental results

方法	R/%	Params/M	mAP@0.5/%
SSD	56.40	93.70	69.50
YOLOV5	72.26	27.20	78.79
YOLOV7	74.90	142.10	72.58
YOLOV8	79.72	42.60	80.14
文献[10]	75.62	244.12	74.68
文献[11]	79.24	46.40	77.64
YOLOV7-tiny	78.74	11.70	79.64
本文算法	83.70	7.74	83.14

图11 改进前后算法损失函数对比

Fig. 11 Comparison of loss functions before and after algorithm improvement

图12 道路病害检测对比图((a)道路污染；(b)横向裂缝；(c)封道施工&裂缝修补；(d)坑槽修补；(e)龟裂)

Fig. 12 The comparison chart of road anomaly detection ((a) Road surface pollution; (b) Transverse cracks; (c) Road closure construction & Crack repair; (d) Pothole repair; (e) Crack)

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基于改进YOLOv7-tiny的道路病害检测算法

Road defect detection algorithm based on improved YOLOv7-tiny

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