计算机技术与发展2025，Vol.35Issue(10)：35-42,8.DOI:10.20165/j.cnki.ISSN1673-629X.2025.0141
基于YOLOv8改进的道路缺陷检测算法
Improved Road Defect Detection Algorithm Based on YOLOv8
摘要
Abstract
Aiming at the contradiction between lightweight and detection accuracy in road defect detection,we propose a balanced model IMD-YOLOv8 based on the YOLOv8 model.Firstly,by fusing the inversion residual structure iRMB and efficient multi-scale attention EMA,the C2F-IEMA module was designed to replace the original C2f module.Under the premise of maintaining a similar number of parameters,the cross-scale feature recombination was used to enhance the ability of disease feature extraction and significantly improve the accuracy of small target detection.Secondly,the multi-scale hole attention MSDA was introduced to optimize the local feature interaction,and the detection robustness was further improved through multi-scale semantic aggregation.The fixed structure was replaced by DynamicHead,and the task-aware attention was used to dynamically allocate computing resources,which significantly reduced redundant parameters.Finally,the shape-sensitive ShapeIoU loss function is designed to optimize the regression accuracy by restricting the shape similarity of the bounding box to avoid the introduction of additional computational overhead.Experiments show that IMD-YOLOv8 achieves mAP@0.5 of 89.4%on the RDD2022 dataset,which is2.9 percentage point higher than that of YOLOv8,reduces the amount of parameters and calculation by 20 percentage point and 3 percentage point respectively,and achieves a FPS of 98.5 f/s,which achieves an effective balance between lightweight and accuracy.关键词
目标检测/YOLOv8/道路缺陷检测/注意力机制/动态检测头Key words
object detection/YOLOv8/road defect detection/attention mechanism/dynamic detection head分类
信息技术与安全科学引用本文复制引用
汪杰,梁平,廖光忠..基于YOLOv8改进的道路缺陷检测算法[J].计算机技术与发展,2025,35(10):35-42,8.基金项目
国家重点研发计划重点专项(2022YFC3300801) （2022YFC3300801）
武汉市知识创新专项-曙光计划项目(2023010201020409) （2023010201020409）
