政大機構典藏-National Chengchi University Institutional Repository(NCCUR):Item 140.119/137164

政大典藏 > College of Informatics > Executive Master Program of Computer Science of NCCU > Theses > Item 140.119/137164

Please use this identifier to cite or link to this item: https://nccur.lib.nccu.edu.tw/handle/140.119/137164

Title:	基於深度學習之印刷電路板缺陷偵測 Detection of PCB Defects Using Deep Learning Approach
Authors:	張家瑋 Chang, Chia-Wei
Contributors:	廖文宏 Liao, Wen-Hung 張家瑋 Chang, Chia-Wei
Keywords:	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 Deep learning Object detection PCB(Printed circuit board) Defect detection Manual visual inspection DIP(Dual In-line Package)
Date:	2021
Issue Date:	2021-09-02 18:17:04 (UTC+8)
Abstract:	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。 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.
Reference:	[1] Joseph Redmon, Ali Farhadi, “YOLOv3: AnIncrementalImprovement”, 8 Apr 2018. [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. [3] J.R.R. Uijlings and K.E.A. van de Sande and T. Gevers and A.W.M.Smeulders , “Selective Search for Object Recognition”, 2012. [4] C. Lawrence ZitnickPiotr Dollár, “Edge Boxes : Locating Object Proposals from Edges”, 2014. [5] Peiyun Hu, Deva Ramanan Robotics Institute, "Finding Tiny Faces", 2017. [6] Wei Liu, Dragomir Anguelov, Dumitru Erhan, Christian Szegedy, Scott Reed, Cheng-Yang Fu, Alexander C. Berg, “SSD: Single Shot MultiBox Detector”, 29 Dec 2016. [7] Joseph Redmon, Santosh Divvala, Ross Girshick, Ali Farhadi, “You Only Look Once: Unified, Real-Time Object Detection”, 8 Jun 2015. [8] T.-Y. Lin, P. Goyal, R. Girshick, K. He, and P. Doll´ar, “Focal loss for dense object detection”, 2017. [9] Ondrej Simecek, TRI Marketing Dept, “TRI White Paper Introduction to Automated Optical Inspection (AOI) Technology Ver. 2.0.0”. [10] 賴威豪,曾紹崟, "Defect Classification of Printed Circuit Board Based on Deep Convolutional Neural Networks", 2017. [11] Peng Wei, Chang Liu, Mengyuan Liu, Yunlong Gao, Hong Liu, “CNN-based reference comparison method for classifying bare PCB defects”, ACAIT 2018. [12] Alexey Bochkovskiy, Chien-Yao Wang, Hong-Yuan Mark Liao, “YOLOv4: Optimal Speed and Accuracy of Object Detection”, 2020.
Description:	碩士國立政治大學資訊科學系碩士在職專班 107971013
Source URI:	http://thesis.lib.nccu.edu.tw/record/#G0107971013
Data Type:	thesis
DOI:	10.6814/NCCU202101352
Appears in Collections:	[Executive Master Program of Computer Science of NCCU] Theses

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