帧(网络)
计算机科学
重型的
航程(航空)
工程类
数据挖掘
电信
汽车工程
航空航天工程
作者
Chengshui Yu,Yue Liu,Yuan Cao,Yongkui Sun,Shuai Su,Weifeng Yang,Wenkun Wang
标识
DOI:10.1088/1361-6501/ad3a05
摘要
Abstract With the high speed and heavy duty of railway transportation, internal flaw detection of railway rails has become a hot issue. Existing rail flaw detection systems have problems of low detection accuracy and occasional missed flaw detection. In this paper, a high-precision flaw detection based on data augmentation and YOLOv8 improvement is proposed. Firstly, three data augmentation algorithms based on the characteristics of B-scan images are designed to enrich the dataset of rail flaws. Then, the small target detection layer and the cross-layer connectivity module are added to capture more information for small targets. Finally, the introduction of dynamic weights to coordinate attention can adjust the attentional weights and capture long-range information. The experimental results show that the mAP50 of the model after data enhancement and algorithm improvement is 97.9%, which is improved by 4.4% from the baseline model, and the frame per second is 64.52. The proposed method effectively detects many typical flaws, including the railhead flaw, rail jaw flaw, screw hole crack, and bottom flaw, which can provide technology supports for on-site maintenance staff.
科研通智能强力驱动
Strongly Powered by AbleSci AI