A refined YOLOv8-based algorithm for lightweight pavement disease detection
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.
deep learningpavement disease detectionYOLOv8nattention mechanismlightweight algorithm
NETL
NSTL
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