内部线程缺陷生成算法和检测系统基于生成对抗网络,你只看一次
Zhihao Jiang1, Xiaohan Dou1, Xiaolong Liu1
1School of Electronic lnformation and Electrical Engineering, Yangtze University, Jingzhou 434100, China.
Sensors (Basel, Switzerland)
|September 14, 2024
概括
本研究引入了一种新的方法,用于检测内部线程缺陷,使用生成对抗网络 (GAN) 来增强数据,并使用YOLO算法进行检测,提高工业检查准确度.
科学领域:
- 工业检查 工业检查 工业检查 工业检查
- 机械工程 机械工程
- 计算机视觉 计算机视觉 计算机视觉
背景情况:
- 准确检测线程质量对于工业环境中的机械性能至关重要.
- 目前的机器视觉方法由于几何复杂性和有限的训练数据,难以检测内部线程缺陷.
- 挑战包括有效的检测和获得足够多的高质量的培训样本.
研究的目的:
- 开发一种有效的机器视觉方法,用于内部线程缺陷检测.
- 解决现有方法中培训数据不足的局限性.
- 提高工业线程质量检查的准确性和效率.
主要方法:
- 为内部线程提出了一种新的图像获取结构.
- 使用生成对抗网络 (GAN) 开发了一个数据增强算法,以创建高质量的训练数据集.
- 采用YOLO (你只看一次) 算法来检测缺陷.
- 进行了多指标评估,并将结果与外部线程进行了比较.
主要成果:
- 使用GANs实现了高相似性的内部线程图像生成.
- 对于内部线程 (94.27%) 和外部线程 (93.92%),YOLO算法显示了高检测准确度.
- 有效地识别了内部线程缺陷,克服了以前的限制.
结论:
- 拟议的方法结合了GAN用于数据增强和YOLO用于检测,显著提高了内部线程缺陷检测.
- 这种方法为工业检查提供了强大的解决方案,改善了机械性能保证.
- 该技术提供了一种可行且准确的方法,用于在涉及线程的制造过程中进行质量控制.
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