Research on road disease detection based on GOLD-YOLO improved YOLOv5 model
With the increase in the number of motor vehicles and road loads,the problem of road diseases is becoming increas-ingly serious.It is necessary to timely detect and identify various diseases to ensure road traffic safety.However,the FPN informa-tion fusion method adopted by the traditional YOLOv5 may lead to information loss.Therefore,the traditional YOLOv5 feature fu-sion module is improved by combining the Gather and Distribution module in Huawei GOLD-YOLO.The experimental results show that the optimized YOLOv5 algorithm significantly reduces the number of iterations required for convergence during model train-ing,from 590 to 366,greatly improving training speed.Meanwhile,overall mAP@0.5 It has also increased from 87.4%to 88.7%.
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