The DOI matches its Crossref record; 10 of the 10 references it cites were found in a registry or an index.
TDD‐net: a tiny defect detection network for printed circuit boards
Open accessAbstract
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.
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Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
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Cited by
Zeng, N., Wu, P., Wang, Z., Li, H., Liu, W., & Liu, X. (2022) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2022.3153997 · Cited by 523
PCB defect detection algorithm based on CDI-YOLO
Xiao, G., Hou, S., & Zhou, H. (2024) · Scientific Reports · doi:10.1038/s41598-024-57491-3 · Cited by 113
Metadata
- Authors
- Ding, R., Dai, L., Li, G., & Liu, H.
- Venue
- CAAI Transactions on Intelligence Technology
- Published
- 2019-05-31
- Volume
- 4
- Issue
- 2
- Pages
- 110-116
- ISSN
- 2468-2322 (ISSN-L)
- 2468-6557
- OpenAlex
- W2941001797
- Cited by
- 440
- References
- 10
- FWCI
- 14.87
- Type
- journal article
- Language
- en
- Open access
- gold
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