基于轻量级YOLOv5的带钢表面缺陷检测
Yongping Zhang1, Sijie Shen1, Sen Xu1
1School of Information Engineering, Yancheng Institute of Technology, Yancheng, China.
Frontiers in neurorobotics
|October 20, 2023
概括
一个新的轻量级YOLOv5算法通过平衡准确性和速度来改善钢带表面缺陷检测. 这种方法提高了检测能力,减少了工业应用的错误和计算负载.
科学领域:
- 材料科学 材料科学 材料科学
- 计算机科学 计算机科学
- 人工智能的人工智能
背景情况:
- 钢带表面缺陷检测的深度学习方法面临着目标丢失,错误报警和计算需求等挑战.
- 现有的算法难以平衡检测准确度和实时处理速度.
研究的目的:
- 开发一个轻量级的YOLOv5算法用于钢带表面缺陷检测,优化精度和速度之间的权衡.
- 解决工业缺陷检测当前深度学习模型的局限性.
主要方法:
- 推出了GSConv,一个高效的轻型卷积层,以及Slim Neck架构,以减少参数并提高速度.
- 集成SimAM,一个非参数的注意力机制,进入子,以提高检测准确度.
- 采用SIoU损失函数用于回归预测,以加快收和提高效率.
主要成果:
- 拟议的YOLOv5-GSS算法在NEU-DET数据集上实现了83.8%的平均平均精度 (mAP).
- 该模型的检测速度为100 Hz,比标准YOLOv5.5快3.8 Hz.
- 在准确度上,超过了基线YOLOv5方法的2.9%.
结论:
- YOLOv5-GSS算法有效地平衡了检测准确度和速度,用于检测钢带表面缺陷.
- 优化策略,包括GSConv,SimAM和SIoU损失,被证明是有效的和优于现有的方法.
- 开发的模型为工业表面缺陷检查提供了实用和高效的解决方案.
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