实时工业安全自动化使用YOLO架构,利用多种多样的色域
Natasha Pati1, Atul Sharma1, Mahendra Kumar Gourisaria1
1School of Computer Engineering, KIIT Deemed to be University, Bhubaneswar, Odisha, 751024, India.
Scientific reports
|February 4, 2026
概括
本研究评估了YOLO对象检测模型,用于实时检测接缺陷. YOLOv8显示了提高的准确性和速度,特别是在RGB色彩空间,使其成为工业安全的理想选择.
科学领域:
- 计算机视觉 计算机视觉
- 机器学习 机器学习
- 工业检查 工业检查 工业检查
背景情况:
- 对象检测对于计算机视觉和工业应用至关重要.
- 它识别和定位对象,提供超出简单分类的界限框.
- 实时检测对于工业安全和质量控制至关重要.
研究的目的:
- 评估用于实时检测接缺陷的YOLO物体检测模型 (YOLOv3,YOLOv5,YOLOv8).
- 分析不同颜色空间 (RGB,HSV,LAB,YCbCr) 对检测性能的影响.
- 评估YOLOv8在工业环境中的有效性.
主要方法:
- 使用实时框架来评估YOLOv3,YOLOv5和YOLOv8.
- 在RGB,HSV,LAB和YCbCr颜色空间中分析了性能.
- 关键指标包括准确性和推断速度,重点是mAP@0.5.5.
主要成果:
- 与之前的版本相比,YOLOv8在准确性和推断速度方面取得了显著的改进.
- 在RGB色彩空间中,YOLOv8.2的正常化mAP@0.5的值为0.592.
- 在具有挑战性的视觉条件下,YOLOv8的性能特别有效.
结论:
- YOLOv8在实时接缺陷检测方面非常有效,性能优于之前的版本.
- 颜色空间的选择,特别是RGB,会影响检测结果.
- 由于YOLOv8的效率和精度,它适用于各种工业安全应用.
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