AI驱动的多阶段计算机视觉系统用于激光雕刻工业铭牌的缺陷检测
AI-Driven Multi-Stage Computer Vision System for Defect Detection in Laser-Engraved Industrial Nameplates
AI总结：
本文提出了一种AI驱动的多阶段计算机视觉系统，集成YOLOv7、Tesseract OCR和ResVAE，用于检测激光雕刻工业铭牌中的缺陷，实现91.33%准确率和100%召回率，有效提升制造质量控制。
摘要：
工业制造中的自动化缺陷检测对于维持产品质量和减少生产错误至关重要。在空气盘式制动器制造中，确保激光雕刻铭牌的精度对于准确的产品识别和质量控制至关重要。雕刻错误（如错印或字符缺失）会损害美观性和功能性，导致材料浪费和生产延误。本文提出了一个AI驱动的计算机视觉系统的概念验证，该系统检查并验证激光雕刻铭牌，检测标志和字母数字字符串中的缺陷。该系统集成了使用YOLOv7的物体检测、使用Tesseract的光学字符识别（OCR）、通过残差变分自编码器（ResVAE）的异常检测以及其他计算机视觉方法，以实现多阶段的全面检查。实验结果证明了该系统的有效性，达到了91.33%的准确率和100%的召回率，确保有缺陷的铭牌被一致地检测和处理。该解决方案突出了AI驱动的视觉检查在增强质量控制、减少人工检查工作量和提高整体制造效率方面的潜力。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
