Research on Recognition Method of Road Surface Disease Image Based on YOLOv5
In highway maintenance,the existing methods for detecting road surface diseases mostly rely on automated data collection and manual identification,which greatly reduces the efficiency of road maintenance.To improve the efficiency of road surface disease recognition,this paper proposes an improved YOLOv5-based algorithm for road surface disease image recognition.The CA mechanism and SPPCSPC structure are introduced into the backbone of YOLOv5.The CA mechanism enhances the receptive field of the model and accurately localizes the regions of interest.The SPPCSPC structure enables the algorithm to adapt to different image resolutions and improves the recognition speed.In terms of anchor boxes,the k-means algorithm in YOLOv5 is replaced with k-means++to make the anchor boxes better fit the sizes of real annotated boxes in the dataset.Experimental results show that compared to the original model,the proposed method achieves a 8.1％improvement in accuracy and a 12.8％improvement in detection speed in a dataset consisting of 56 879 images of 9 types of road surface diseases.It also outperforms methods such as Faster R-CNN and YOLOv3.
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