TDD‐net: a tiny defect detection network for printed circuit boards. Issue 2 (31st May 2019)

• Record Type: Journal Article
• Authors: Ding, Runwei; Dai, Linhui; Li, Guangpeng; Liu, Hong
• Is Part Of: CAAI transactions on intelligence technology. Volume 4:Issue 2(2019)
• Page Start: 110
• Page End: 116
• Publication Date: 2019-05-31
• Subjects: neural nets -- quality control -- production engineering computing -- data mining -- printed circuits -- learning (artificial intelligence) -- computer vision -- pattern clustering -- feature extraction; TDD‐Net strengthens -- PCB defect dataset show -- tiny defect detection network -- printed circuit boards -- PCB defect detection -- complex PCBs -- diverse PCBs -- deep convolutional networks; C5260B Computer vision and image processing techniques -- C6170K Knowledge engineering techniques -- C7480 Production engineering computing -- E0410D Industrial applications of IT; Artificial intelligence -- Periodicals; Computer science -- Periodicals; Artificial intelligence; Computer science; Electronic journals; Periodicals; 006.305
• DOI: 10.1049/trit.2019.0019
• Languages: English
• ISSNs: 2468-6557

Abstract

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.