辽宁工程技术大学学报（自然科学版）2025，Vol.44Issue(1)：102-112,11.
改进YOLOv5的复杂场景下水泥路面病害检测
Disease detection of cement pavement based on improved YOLOv5 in complex scenarios
摘要
Abstract
Aiming at the problems of lack of domestic cement pavement disease detection data sets,small scale,single scene,and insufficient generalization ability of deep learning algorithms in complex scenes,a pavement disease detection algorithm based on improved YOLOv5 is proposed.A cement pavement disease detection dataset containing 11 862 images was collected and constructed,covering 3 most common disease types in 9 scenarios.The prior anchor frame of model training is obtained by combining the K-Means clustering algorithm measured by IoU and genetic algorithm.In the feature enhancement stage,a lightweight upsampling module(CARAFE)is introduced to reduce the information loss in the feature recombination process.A multi-dimensional collaborative attention module(MCA)considering channel,height and width dimensions is introduced to enhance the discrimination of multi-scale disease features.The experimental results show that under the premise of maintaining fast inference speed,the average of F1 score and average precision(mAP)of the proposed algorithm reach 75.5%and 81.6%,respectively,which are better than the five mainstream target detection algorithms.The example analysis shows that the pavement disease detection algorithm based on improved YOLOv5 can meet the actual needs of large-scale cement pavement disease intelligent detection and damage condition evaluation.关键词
水泥路面/深度学习/ISTD-PDD3数据集/病害检测/ISTD-YOLO模型Key words
cement pavement/deep learning/ISTD-PDD3 data set/disease detection/ISTD-YOLO model分类
天文与地球科学引用本文复制引用
张在岩,宋伟东,邬嘉晨..改进YOLOv5的复杂场景下水泥路面病害检测[J].辽宁工程技术大学学报（自然科学版）,2025,44(1):102-112,11.基金项目
国家自然科学基金项目(42071343) （42071343）
黑龙江省省属本科高校基本科研业务费(2020-KYYWF-0690) （2020-KYYWF-0690）
