基于YOLOv5的行李运输系统轮对裂纹检测算法
Wheelset Crack Detection Algorithm of Baggage Transportation System Based on YOLOv5
方浩楠 1李登鹏1
作者信息
- 1. 中国民航大学 电子信息与自动化学院,天津 300300
- 折叠
摘要
为了检测机场行李运输系统中轮对存在的裂纹,提出了一种基于 YOLOv5 的机场行李运输系统轮对裂纹检测方法.该方法针对裂纹细小、密集等特点采取了以下措施:在 YOLOv5 网络的 Head 部分使用 SIoU替换了原有的CIoU;在Backbone部分加入了 SE 注意力机制;在 Neck 部分引入了 Swin-Trans-former模块;在整个 YOLOv5 网络中使用 SPD-Conv替代了传统的 Conv 卷积模块;在图像的预处理方面,使用了图像分割与子图反向拼接技术.通过这些改进,有效地改善了 YOLOv5 对于细小、密集裂纹的特征提取能力,相较于传统的 YOLOv5 算法,裂纹检测的能力得到了有效提升.
Abstract
In order to detect the cracks existing in the wheelset of airport baggage transportation system,a new wheelset crack detection method based on YOLOv5 was proposed.In view of the characteristics of small and dense cracks,the follow-ing measures were taken.The original CIoU is replaced by SIoU in the Head part of YOLOv5 network.Then the SE atten-tion mechanism is added to the Backbone part and the Swin-Transformer module is introduced in the Neck part.In addition,The traditional Conv convolution module is replaced by space-to-depth-Conv in the entire YOLOv5 network.In terms of im-age preprocessing,image segmentation and subgraph reverse stitching technology are used.Through these improvements,the feature extraction ability of YOLOv5 for fine and dense cracks is effectively improved,and compared with the traditional YOLOv5 algorithm,the ability of crack detection has been effectively improved.
关键词
裂纹检测/YOLOv5/SPD-Conv(space-to-depth-Conv)/SIoU/注意力机制/图像预处理Key words
crack detection/YOLOv5/SPD-Conv(space-to-depth-Conv)/SIoU/attention mechanism/image preprocessing引用本文复制引用
基金项目
天津市大学生创新创业训练计划()
中国民航大学大学生创新创业训练计划(2022)(202210059165)
出版年
2024
国家科技期刊平台
NSTL
万方数据