混合感应场增强了YOLO与多路径空间金字塔聚合,用于钢表面缺陷检测
Kewen Xia1, Zhongliang Lv1, Chuande Zhou1
1College of Mechanical and Power Engineering, Chongqing University of Science and Technology, Chongqing 401331, China.
Sensors (Basel, Switzerland)
|June 10, 2023
概括
这项研究引入了一种改进的YOLOv5s模型用于钢表面缺陷检测,通过优化特征提取和尺度适应来提高各种缺陷的准确性,例如裂纹和入.
科学领域:
- 材料科学 材料科学 材料科学
- 计算机视觉 计算机视觉
- 人工智能的人工智能
背景情况:
- 由于纹理干扰和尺度变化,钢表面缺陷检测面临低效率和精度的挑战.
- 现有的方法很难有效地处理复杂的纹理和各种缺陷大小.
研究的目的:
- 为了提高钢表面缺陷的检测效率和准确性.
- 解决当前缺陷检测模型中特征提取和规模适应方面的局限性.
主要方法:
- 一个改进的YOLOv5s模型,包含一个重新参数化的大型内核C3模块,以更好地提取特征.
- 一个具有多路径空间金字塔聚合模块的特征融合结构,以适应规模变化.
- 一种新的训练策略,使用规模特定的内核大小来优化受体场适应.
主要成果:
- 对于破裂 (14.4%) 和卷入尺度 (11.1%) 的缺陷,精度得到了显著的改进.
- 提高了包括 (10.5%) 和划伤 (6.6%) 缺陷的检测准确度.
- 实现了76.8%的平均平均精度 (mAP),超过YOLOv5s的8.6%和YOLOv8s的3.7%.
结论:
- 建议改进的YOLOv5s模型有效地克服了钢表面缺陷检测方面的挑战.
- 新的模块和培训策略显著提高了各种缺陷类型和规模的检测准确性和效率.
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