Abstract
Different weather and light conditions will affect the detection of pavement sag. And the camera field of view is limited, can not fully cover the entire highway road surface, resulting in higher difficulty in road sag detection. Therefore, a rapid visual detection method for highway pavement sag based on improved Yolov7-tiny is proposed. The structure elements are determined, and the small noise points in the image are removed by etching operation, and the morphology of the highway surface is completed. Input the processed highway pavement sag image into YOLOv7-tiny network, improve the input module and backbone network, and output the irregular shape of the sag image target. Fuzzy C-Means (FCM) clustering algorithm is introduced and objective function is established to realize the morphological feature visual detection of highway pavement depression. The experimental results show that: The method has smaller entropy, larger fuzzy coefficient, higher peak signal-to-noise ratio, and clearer features of pavement depression images.
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