康耐视 VIDI 套件：汽车行业的深度学习
技术
- 分析与建模 - 计算机视觉软件
- 分析与建模 - 机器学习
适用行业
- 汽车
适用功能
- 离散制造
- 质量保证
用例
- 计算机视觉
- 视觉质量检测
服务
- 软件设计与工程服务
挑战
活塞的压缩环在往复式发动机中具有多种功能，包括密封燃烧室和调节油耗。由于活塞的反射金属表面，压缩环上的缺陷很难检测到。活塞的圆柱形状有时会在图像中模糊和失焦。金属表面纹理的正常变化是制造过程的一部分，其中一些变化是可以接受的，包括锈斑、白色区域以及表面裂纹和裂缝。但是一些影响活塞性能并威胁气缸压缩水平的长划痕是真正的缺陷。检测系统必须能够忽略压缩环表面上的正常变化和微不足道的异常，同时识别较长的划痕缺陷。
将这种复杂的检查编程为基于规则的算法将需要复杂的缺陷数据库。虽然手动检测更灵活，但速度太慢。
解决方案
康耐视 ViDi 套件提供了一种有效的检测解决方案，将人类识别微小变化的能力与自动化系统的可靠性、一致性和速度相结合。在监督模式下使用 Cognex ViDi Red-Check 工具时，工程师会在一组具有代表性的已知“通过”和“失败”压缩环的样本图像上训练基于深度学习的软件。技术人员对具有较长划痕的已知“不合格”样品和具有正常变化和可接受缺陷（包括锈斑和小裂纹）的“良好”样品的图像进行注释。基于这些图像，康耐视 ViDi 套件将了解活塞的自然形态和表面纹理以及划痕的正常外观。在验证测试期间，可以将其他图像添加到训练图像组合中，以反映其他示例并帮助优化系统。在训练阶段和验证阶段，可以不断调整参数，直到训练好的模型正确检测并分割出所有包含长划痕的图像。
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