Pavement crack detection is an important task to ensure road safety and realize timely repair of road damage.Aim-ing at the problems of low detection accuracy and inaccurate positioning in existing pavement detection,an improved pavement crack detection algorithm YOLOV8-pavement based on YOLOv8 was proposed.Firstly,off-line data enhancement is carried out on the data set during model training to improve the generalization ability of the model.Secondly,Focal Modulation(FM)module is added at the end of the backbone network to capture the long distance dependence and context information in the image to adapt to the large span and slender features of the cracked object.Finally,CSPStage(CS)module is used in the neck network to improve the performance of feature expression and reduce the number of parameters and computation.Experiments show that compared with the original yolov8n model,mAP50 increases by 1.2 percentage points,while the parameter number and calculation amount of the model decrease by 3 percentage points and 4.9 percentage points respectively.The algorithm has a good detection effect.
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