A refined YOLOv8-based algorithm for lightweight pavement disease detection

doi:10.11996/JG.j.2095-302X.2024050892

Abstract

Abstract:

Road surface defect detection is a crucial task for repairing road damage and ensuring driving safety. To address the issues of low detection accuracy, high costs, large model parameters, and the difficulty in applying existing road surface defect detection algorithms to mobile terminal devices, a lightweight detection algorithm, YOLOv8n-GSBP, based on the improved YOLOv8n model, was proposed. Firstly, the C2f-GhostNetv2 module was introduced into the backbone network to maintain detection accuracy while achieving model lightweight. Additionally, the SimAM module was added after the SPPF module to enhance the network’s ability to extract road surface defect features and distinguish them from background environmental features. Secondly, the neck network was replaced with the BiFPN structure, and the model’s multi-scale feature fusion capability was enhanced while addressing significant differences in road surface defect scales to improve precision and robustness. Finally, the head was improved by the parameter-sharing principle, and the spatial channel reconstruction convolutional module SCConv was introduced to achieve lightweight improvement of the detection head while reducing model parameters and computational complexity. The experimental results on the RDD2022 dataset showed that the mAP50 of YOLOv8n-GSBP road surface disease detection method was 0.3% higher than that of the YOLOv8n; however, the parameters were reduced by 55.6% and the computational complexity was reduced to 36.7%. Furthermore, through comparisons with other mainstream object detection algorithms, we further validated both effectiveness and superiority of our proposed algorithm.

Key words: deep learning, pavement disease detection, YOLOv8n, attention mechanism, lightweight algorithm

CLC Number:

HU Fengkuo, YE Lan, TAN Xianfeng, ZHANG Qinzhan, HU Zhixin, FANG Qing, WANG Lei, MAN Xiaofeng. A refined YOLOv8-based algorithm for lightweight pavement disease detection[J]. Journal of Graphics, 2024, 45(5): 892-900.

Figures/Tables 13

References 22

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名称	参数
操作系统	Windows 10
CPU	13th Gen Intel(R) Core(TM) i5-13400F
显卡	Nvidia RTX3060
显存	12 GB
编程语言	Python3.8
深度学习框架	Pytorch 1.12.1+CUDA 11.3.1
优化函数	SGD
训练轮数	300
初始学习率	0.01
批量	16

名称	参数
操作系统	Windows 10
CPU	13th Gen Intel(R) Core(TM) i5-13400F
显卡	Nvidia RTX3060
显存	12 GB
编程语言	Python3.8
深度学习框架	Pytorch 1.12.1+CUDA 11.3.1
优化函数	SGD
训练轮数	300
初始学习率	0.01
批量	16

Algorithms	mAP50/%	mAP50:90/%	F1	GFLOPs/G	Params/M
YOLOv8n	57.0	29.4	57.8	8.1	3.0
YOLOv8n+BLRA	56.7	28.5	57.6	8.3	3.42
YOLOv8n+TA	57.2	28.6	57.9	8.2	3.2
YOLOv8n+CA	57.4	28.9	58.3	8.1	3.0
YOLOv8n+SimAM	57.5	29.1	58.4	8.1	3.0

Algorithms	mAP50/%	mAP50:90/%	F1	GFLOPs/G	Params/M
YOLOv8n	57.0	29.4	57.8	8.1	3.0
YOLOv8n+BLRA	56.7	28.5	57.6	8.3	3.42
YOLOv8n+TA	57.2	28.6	57.9	8.2	3.2
YOLOv8n+CA	57.4	28.9	58.3	8.1	3.0
YOLOv8n+SimAM	57.5	29.1	58.4	8.1	3.0

Algorithms	mAP50/%	mAP50:90/%	F1	GFLOPs/G	Params/M
YOLOv8n	57.0	29.4	57.8	8.1	3.0
v8n+RepConv	57.3	29.6	58.1	8.5	3.1
v8n+EMSConv	56.6	29.2	57.3	6.0	2.6
v8n+SCConv	56.8	29.3	57.6	5.8	2.6