基於深度學習之印刷電路板缺陷偵測

本論文旨在探究以人工智慧方式取代工廠生產線使用的人工目視檢測的可行性，採用基於深度學習技術的物件偵測框架為主要演算法；以六種在產線的雙列直插封裝（DIP）階段中常見的印刷電路板缺陷為主要偵測類別，然而因印刷電路板缺陷影像資料取得不易，本研究試圖在有限的資料量中使用不同比例和組合的資料集做分析，從中訓練並取得結果較佳的模型，進而導入於本研究的DIP AI系統中。DIP AI系統包含了Inference Module、Data Server、Training System三個子系統，經實驗測試此DIP AI系統能成功的在工廠產線的DIP階段中檢測出印刷電路板缺陷，用以幫助產線操作員快速地進行下一階段的修補作業，改善工廠產線的生產作業流程。

關鍵字

深度學習；物件偵測；印刷電路板；缺陷偵測；人工目視檢測；雙列直插封裝

並列摘要

The objective of this thesis is to explore the feasibility of replacing the manual inspection used in the factory production line with AI (Artificial intelligence). The research method is based on the object detection framework in deep learning. Six common printed circuit board (PCB) defects in the DIP stage of the production process have been identified as the main target for the detection task. Due to the difficulty of collecting and labeling images with PCB defects, this research experiments with different proportions and combinations of data to arrive at a robust model, which is then implemented and integrated into a DIP system that consists of three components: Inference Module, Data Server, and Training System. The experimental results demonstrate that this DIP AI system can successfully detect the PCB defects, and help the operators to quickly move on to the next repair stage, thereby improving the process of the factory production line.

並列關鍵字

Deep learning ； Object detection ； PCB(Printed circuit board) ； Defect detection ； Manual visual inspection ； DIP(Dual In-line Package)

參考文獻

[1] Joseph Redmon, Ali Farhadi, “YOLOv3: AnIncrementalImprovement”, 8 Apr 2018.

Google Scholar

[2] Licheng Jiao, Fellow, IEEE, Fan Zhang, Fang Liu, Senior Member, IEEE, Shuyuan Yang, Senior Member, IEEE, Lingling Li, Member, IEEE, Zhixi Feng, Member, IEEE, and Rong Qu, Senior Member, IEEE, “A Survey of Deep Learning-based Object Detection”, 10 Oct 2019.