计算机与数字工程2024，Vol.52Issue(3)：852-857,6.DOI:10.3969/j.issn.1672-9722.2024.03.037
有雾场景下的道路路面病害检测算法
Recognition Algorithm of Road Pavement Disease in Foggy Scene
摘要
Abstract
Aiming at the problems of over-fitting,poor robustness and low accuracy of the target detection model based on deep learning throughout the process of road disease detection in foggy scene,a foggy road disease detection algorithm based on AECR-YOLOv5 fusion mechanism is proposed.Firstly,according to the characteristics of road pavement diseases,a data set expan-sion method depend on disease category is designed,and the expanded training set is obtained by this method.Meanwhile,clear picture and the depth picture after bilateral filtering are synthesized on the basis of atmospheric scattering model,so as to enhance the credibility of the synthetic foggy picture.Then,according to the uncertainty of task dependence,the loss functions of defogging network and detection network are weighted and fused to obtain the coupling loss function and promote the effective fusion of them.Finally,experiments are carried out by changing the input and network structure.Results show that when the input is the extended foggy picture training set and the network structure is AECR-YOLOv5 fusion model,its mAP on the test set is higher than other combination methods.关键词
路面病害检测/深度学习/去雾/YOLOv5/对比学习Key words
road disease detection/deep learning/defogging/YOLOv5/comparative learning分类
信息技术与安全科学引用本文复制引用
赵呈祥,祁云嵩,丁健宇..有雾场景下的道路路面病害检测算法[J].计算机与数字工程,2024,52(3):852-857,6.基金项目
中国高校产学研创新基金项目(编号:2019ITA01047)资助. （编号:2019ITA01047）
