AI-Driven Multi-Stage Computer Vision System for Defect Detection in Laser-Engraved Industrial Nameplates

2025-03-05

AI Summary

To address the challenge of detecting fine-grained defects—such as logo misalignment and character omissions—in laser-engraved nameplates of air disc brakes, this paper proposes a three-stage collaborative vision system: YOLOv7 for precise localization of logo and character regions, Tesseract for character-level structured optical character recognition (OCR), and ResVAE (Residual Variational Autoencoder) to model the distribution of normal samples for unsupervised anomaly detection. This architecture is the first to integrate object detection, OCR, and residual variational autoencoding, thereby balancing high discriminative accuracy with strong unsupervised generalization capability. Evaluated on real production-line data, the system achieves 91.33% classification accuracy and 100% recall—ensuring zero defect漏检—and significantly reduces scrap rate and manual reinspection costs.

Technology Category

• Computer Vision: Object Detection & Categorization
• Machine Learning: Calibration & Uncertainty Quantification
• Intelligent Robots: Multimodal Perception & Sensor Fusion

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Economics, Online Markets and Human Computation: LLM based quality controls for crowd work

Abstract

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.

Problem

• Automated defect detection in laser-engraved industrial nameplates.
• Ensuring precision in product identification and quality control.
• Reducing material waste and production delays through AI-driven inspection.

Innovation

• AI-driven multi-stage defect detection system
• Integrates YOLOv7, Tesseract OCR, and ResVAE
• Achieves 91.33% accuracy and 100% recall