DeepGD3: Unknown-Aware Deep Generative/Discriminative Hybrid Defect Detector for PCB Soldering Inspection

Ching Wen Ma, Yanwei Liu

研究成果: Conference article同行評審

摘要

We present a novel approach for detecting soldering defects in Printed Circuit Boards (PCBs) composed mainly of Surface Mount Technology (SMT) components, using advanced computer vision and deep learning techniques. The main challenge addressed is the detection of soldering defects in new components for which only samples of good soldering are available at the model training phase. To address this, we design a system composed of generative and discriminative models to leverage the knowledge gained from the soldering samples of old components to detect the soldering defects of new components. To meet industrial quality standards, we keep the leakage rate (i.e., miss detection rate) low by making the system "unknown-aware" with a low unknown rate. We evaluated the method on a real-world dataset from an electronics company. It significantly reduces the leakage rate from 1.827% ± 3.063% and 1.942% ± 1.337% to 0.063% ± 0.075% with an unknown rate of 3.706% ± 2.270% compared to the discriminative and generative approaches, respectively.

原文English
頁(從 - 到)1326-1335
頁數10
期刊Proceedings of Machine Learning Research
216
出版狀態Published - 2023
事件39th Conference on Uncertainty in Artificial Intelligence, UAI 2023 - Pittsburgh, United States
持續時間: 31 7月 20234 8月 2023

指紋

深入研究「DeepGD3: Unknown-Aware Deep Generative/Discriminative Hybrid Defect Detector for PCB Soldering Inspection」主題。共同形成了獨特的指紋。

引用此