Open AccessConvolutional neural network‐based multi‐label classification of PCB defects
Author(s) -
Zhang Linlin,
Jin Yongqing,
Yang Xuesong,
Li Xia,
Duan Xiaodong,
Sun Yuan,
Liu Hong
Publication year - 2018
Publication title -
the journal of engineering
Language(s) - English
Resource type - Journals
ISSN - 2051-3305
DOI - 10.1049/joe.2018.8279
Subject(s) - convolutional neural network , computer science , artificial intelligence , binary classification , task (project management) , pattern recognition (psychology) , multi label classification , deep learning , multiclass classification , contextual image classification , binary number , function (biology) , machine learning , identification (biology) , image (mathematics) , engineering , support vector machine , mathematics , botany , arithmetic , systems engineering , evolutionary biology , biology
Due to the rapid development of printed circuit board (PCB) design technology, inspection of PCB surface defects has become an increasingly critical issue. The classification of PCB defects facilitates the root causes of detects’ identification. As PCB defects may be intensive, the actual PCB classification should not be considered as a binary or multi‐category problem. This type of problem is called multi‐label classification problem. Recently, as one of the deep learning frameworks, a convolutional neural network (CNN) has a major breakthrough in many areas of image processing, especially in the image classification. This study proposes a multi‐task CNN model to handle the multi‐label learning problem by defining each label learning as a binary classification task. In this study, the multi‐label learning is transformed into multiple binary classification tasks by customising the loss function. Extensive experiments demonstrate that the proposed method achieves great performance on the dataset of defects.