用AI多阶段检测激光刻字铭牌缺陷，准确率超91%。
AI-Driven Multi-Stage Computer Vision System for Defect Detection in Laser-Engraved Industrial Nameplates
- 分三步：先定位目标，再识别文字，最后用自编码器查异常
- 检测准确率91.33%，召回率100%，漏检为零
- 适合工业质检场景，减少人工检查负担
工业制造中的自动缺陷检测对保障产品质量至关重要。在空气盘式制动器生产中，激光刻字铭牌的精度直接影响产品识别与质量控制。刻字错误如错印或缺字会损害外观与功能，造成材料浪费和生产延误。本文提出一种AI驱动的计算机视觉系统，用于检测激光刻字铭牌上的缺陷，涵盖图案与字符字符串。系统结合YOLOv7目标检测、Tesseract光学字符识别（OCR）以及基于残差变分自编码器（ResVAE）的异常检测等多阶段方法，实现全流程视觉检验。实验表明，该系统在测试中达到91.33%的准确率与100%的召回率，确保所有缺陷铭牌均可被有效识别与处理。该方案展示了AI视觉检测在提升质量控制、降低人工成本及优化生产效率方面的潜力。
原文摘要 · Abstract (English)
Automated defect detection in industrial manufacturing is essential for maintaining product quality and minimizing production errors. In air disc brake manufacturing, ensuring the precision of laser-engraved nameplates is crucial for accurate product identification and quality control. Engraving errors, such as misprints or missing characters, can compromise both aesthetics and functionality, leading to material waste and production delays. This paper presents a proof of concept for an AI-driven computer vision system that inspects and verifies laser-engraved nameplates, detecting defects in logos and alphanumeric strings. The system integrates object detection using YOLOv7, optical character recognition (OCR) with Tesseract, and anomaly detection through a residual variational autoencoder (ResVAE) along with other computer vision methods to enable comprehensive inspections at multiple stages. Experimental results demonstrate the system's effectiveness, achieving 91.33% accuracy and 100% recall, ensuring that defective nameplates are consistently detected and addressed. This solution highlights the potential of AI-driven visual inspection to enhance quality control, reduce manual inspection efforts, and improve overall manufacturing efficiency.
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