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Content Provider | IET Digital Library |
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Author | Ding, Runwei Dai, Linhui Li, Guangpeng Liu, Hong |
Abstract | Tiny defect detection (TDD) which aims to perform the quality control of printed circuit boards (PCBs) is a basic and essential task in the production of most electronic products. Though significant progress has been made in PCB defect detection, traditional methods are still difficult to cope with the complex and diverse PCBs. To deal with these problems, this article proposes a tiny defect detection network (TDD-Net) to improve performance for PCB defect detection. In this method, the inherent multi-scale and pyramidal hierarchies of deep convolutional networks are exploited to construct feature pyramids. Compared with existing approaches, the TDD-Net has three novel changes. First, reasonable anchors are designed by using k-means clustering. Second, TDD-Net strengthens the relationship of feature maps from different levels and benefits from low-level structural information, which is suitable for tiny defect detection. Finally, considering the small and imbalance dataset, online hard example mining is adopted in the whole training phase in order to improve the quality of region-of-interest (ROI) proposals and make more effective use of data information. Quantitative results on the PCB defect dataset show that the proposed method has better portability and can achieve 98.90% mAP, which outperforms the state-of-arts. The code will be publicly available. |
Starting Page | 110 |
Ending Page | 116 |
Page Count | 7 |
Volume Number | 4 |
e-ISSN | 24682322 |
Issue Number | Issue 2, Jun (2019) |
Alternate Webpage(s) | https://digital-library.theiet.org/content/journals/trit/4/2 |
Alternate Webpage(s) | https://digital-library.theiet.org/content/journals/10.1049/trit.2019.0019 |
Journal | CAAI Transactions on Intelligence Technology |
Publisher | The Institution of Engineering and Technology Chongqing University of Technology Chinese Association for Artificial Intelligence |
Publisher Date | 2019-04-24 |
Access Restriction | Open |
Rights License | Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0/) |
Subject Keyword | Complex PCBs Computer Vision Computer Vision And Image Processing Technique Data Mining Deep Convolutional Network Diverse PCBs Feature Extraction Industrial Application of IT Knowledge Engineering Technique Learning in AI Neural Nets Pattern Clustering PCB Defect Dataset Show PCB Defect Detection Printed Circuit Printed Circuit Boards Production Engineering Computing Quality Control TDD-Net Strengthens Tiny Defect Detection Network |
Content Type | Text |
Resource Type | Article |
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