Research on a Road Defect Detection Method based on Improved YOLOv8

Abstract

Traditional road defect detection methods mainly rely on manual detection, which is inefficient and has many limitations. In order to detect road defects more accurately and quickly, this paper proposes a road defect detection method based on improved YOLOv8. In this paper, a large number of image datasets containing road defects are collected and annotated, and the YOLOv8 algorithm is used to train the model on the dataset, one group uses the original model, one group replaces the convolutional kernel (DCNv2), and one group adds the attention mechanism (CBAM), and then a comparative evaluation is performed based on the training results of the models in each group. The experimental results show that the improved model reflects higher accuracy in road defect detection compared to the untuned model. The model with the replaced convolutional kernel improved about 2% compared to the original model Mean Average Precision (mAP), and the model with the added attention mechanism improved about 3.5% compared to the original model mPA. This study can efficiently identify different types of road defects, which can help road maintenance work to be carried out more scientifically and efficiently, and provide important support for improving road safety and maintenance efficiency.