Real-time road disease detection model based on improved YOLOv4
A real-time detection model of road diseases based on improved YOLOv4 is proposed with the aim of addressing the issues of low detection accuracy,slow detection rate,and missed detection caused by many types and varying scales of road dis-eases.To prevent the poor detection effect brought on by the short Batch Size,the network model first optimizes the normalizing ap-proach in the convolution block and uses group normalization rather than batch normalization.In order to increase detection speed and quantify the quantity of parameters the network model calculates,the convolution block is optimized simultaneously and re-placed by a deep separable convolution block.Finality,the detection head use the adaptive non-maximum suppression algorithm to address the issue of false and missing identification of tiny targets resulting from the fixed non-maximum suppression threshold.The enhanced YOLOv4 algorithm has a detection accuracy mAP value of 86.64%and a detection speed of 37.90 frames per second in road disease detection,according to the testing data.The enhanced method,when compared to the original YOLOv4 algorithm,enhances detection speed by 10.60 frames per second,improves detection accuracy by 2.89 percent point,and successfully re-solves the missed detection phenomena,all of which contribute to the increased practicability of road illness detection.
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