使用光磁粒子检查和实例细分的火车钉的自动裂检测
Haoguang Wang1,2, Wangzhe Du3, Guanhua Xu1,2
1The State Key Laboratory of Fluid Power and Mechatronic Systems, College of Mechanical Engineering, Zhejiang University, Hangzhou, 310027, Zhejiang, China.
Scientific reports
|May 9, 2024
概括
本研究介绍了一种智能系统,用于通过实例细分来检测铁路的裂. 改进的方法提高了密集的检测准确度,多尺度和不立即的裂,提高了火车紧固件的安全性.
科学领域:
- 铁路工程 铁路工程是指铁路工程.
- 非破坏性测试是指非破坏性测试.
- 人工智能的人工智能是人工智能.
背景情况:
- 铁路钉是易受损坏的关键部件,裂会降低承载能力,增加故障风险.
- 光磁粒子缺陷检测 (FMPFD) 是列车紧固件的标准,但手动检查效率低下,容易出现错误.
- 在光钉图像中检测裂是具有挑战性的,因为它们的密度,多尺度和非实时性质.
研究的目的:
- 开发一种智能系统,用于自动检测铁路钉的裂.
- 为了应对光钉图像中密集,多尺度和非实时裂纹的挑战.
- 为了提高钉裂纹检查的可靠性和效率.
主要方法:
- 实例细分方法用于自动检测裂.
- 新型去中心化目标中心和低重叠率的标签方法.
- 对于后处理密集裂纹区域的高斯加权校正.
- 有效的道空间注意力机制用于多尺度裂特征提取.
- 多任务功能学习,用于在非实例化裂区域中改进检测.
主要成果:
- 提出的方法显著超过基线和最先进的算法.
- 实现了86.4%的回忆率和90.3%的平均平均精度 (mAP0.5).
- 开发了一种多功能单线圈非接触复合材料磁化装置,用于各种钉形状.
结论:
- 开发的智能系统可以有效地检测铁路钉的裂,提高安全性.
- 这些新的方法成功地解决了由复杂的裂特性所带来的挑战.
- 该系统为列车紧固件的自动检查提供了宝贵的进步.
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