Multi-scale road defect detection algorithm based on partially deformable convolution
Aiming at the problem of the current road defect detection algorithm for improvement in terms of accuracy and efficiency,a pavement defect detection algorithm YOLOv8-PFMD under dual-branch multi-scale features was proposed in this paper.First,partially deformable convolution(P-DCNv3)was used to replace conventional convolution to improve the feature extraction capability of the model while enhancing its adaptability to different defect deformations;secondly,the more efficient Faster_RFE_Bottleneck module was used in the C2f module,combining the Pconv and RFE structures to make full use of the advantages of the receptive field in the feature map to further reduce the amount of model calculations;then,based on the coordinate attention,a multi-scale dual-branch coordinate attention(MDCA),by expanding the split fusion of dual branches,thereby reducing model parameters and improving the model feature expression ability;finally,the convolution of the two detection heads of YOLOv8n was fused into a depth separable convolution(DSConv),making the model number of parameters was significantly reduced.Experimental results showed that on the RDD2022 data set and Road Damage data set,compared with the original algorithm,the mAP50 of the improved algorithm increased by 8.4％and 7.3％respectively,and the amount of parameters and calculation amount were reduced by 16.7％and 20.7％respectively.On the RDD2022 data set,the algorithm achieved improved results compared to mainstream target detection algorithms such as Faster-RCNN and YOLOv7 in terms of mAP50 and F1 scores.
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