用YOLO+生成模型自动检测电子元件缺陷，解决数据少难题
Automated Defect Detection for Mass-Produced Electronic Components Based on YOLO Object Detection Models
- 用ConSinGAN生成缺陷图像，弥补真实缺陷数据不足
- YOLOv7结合生成数据达95.5%准确率，检测仅需285毫秒
- 适合工厂部署，尤其适用于缺陷样本稀缺的场景
传统工业组件缺陷检测耗时且人力密集，难以保障产品质量。本文针对广泛应用的双列直插封装（DIP）组件，提出一种基于数字相机与深度学习的自动化缺陷检测系统。重点分析两类常见缺陷：表面缺陷和引脚缺陷。由于真实缺陷图像稀缺，难以训练有效模型，为此采用ConSinGAN生成适配规模的数据集用于训练与测试。对比了YOLOv3、v4、v7、v9四种模型，结果表明，结合ConSinGAN的YOLOv7在准确率上达到95.50%，检测时间仅285毫秒，显著优于基于阈值的方法。同时开发了监控与数据采集（SCADA）系统，并设计配套传感器架构。该方法可快速应用于多种缺陷类型或数据匮乏的工业场景。
原文摘要 · Abstract (English)
Since the defect detection of conventional industry components is time-consuming and labor-intensive, it leads to a significant burden on quality inspection personnel and makes it difficult to manage product quality. In this paper, we propose an automated defect detection system for the dual in-line package (DIP) that is widely used in industry, using digital camera optics and a deep learning (DL)-based model. The two most common defect categories of DIP are examined: (1) surface defects, and (2) pin-leg defects. However, the lack of defective component images leads to a challenge for detection tasks. To solve this problem, the ConSinGAN is used to generate a suitable-sized dataset for training and testing. Four varieties of the YOLO model are investigated (v3, v4, v7, and v9), both in isolation and with the ConSinGAN augmentation. The proposed YOLOv7 with ConSinGAN is superior to the other YOLO versions in accuracy of 95.50\%, detection time of 285 ms, and is far superior to threshold-based approaches. In addition, the supervisory control and data acquisition (SCADA) system is developed, and the associated sensor architecture is described. The proposed automated defect detection can be easily established with numerous types of defects or insufficient defect data.
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