Digital Study of Road Damage on Improved YOLOv8 Algorithm
In order to solve the problem that the speed and accuracy of road damage detection based on machine vision are not high,and the optimization effect of algorithm is not significant,CBAM module is introduced into YOLOv8 algorithm,and ablation experiments are carried out to introduce SE and ECA modules separately.The IEEE big data of RDD 2020 is used to train the model,and binary images are obtained through image preprocessing.Compared with YOLOv8 algorithm,the accuracy rate,recall rate,mAP@0.5,mAP@0.5∶0.95 of CBAM-YOLOv8 algorithm have been improved,while the detection accuracy of SE-YOLOv8 algorithm and ECA-YOLOv8 algorithm is lower than that of YOLOv8 algorithm.Furthermore,the superiority of CBAM-YOLOv8 algorithm in road detection is verified through RDD 2022 dataset.The application of CBAM-YOLOv8 algorithm can further improve the speed and accuracy of road damage detection.
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