ArticleScientific reports2024
Defect detection of printed circuit board assembly based on YOLOv5.
Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
What it found
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The trial behind it
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Who cites it
6 citing papers in PubMed.
- MCM-YOLO: A Lightweight Conflict Mitigation Network for Industrial Metal Surface Defect Detection.Sensors (Basel, Switzerland) · 2026Article
- Research on a lightweight model for laser-cut diamond defect detection based on multi-module collaborative optimization.Scientific reports · 2026Article
- Enhanced YOLOv11 framework for high precision defect detection in printed circuit boards.Scientific reports · 2025Article
- An improved EAE-DETR model for defect detection of server motherboard.Scientific reports · 2025Article
- A texture enhanced attention model for defect detection in thermal protection materials.Scientific reports · 2025Article
- Feature optimization-guided high-precision and real-time metal surface defect detection network.Scientific reports · 2024Article
Corrections and comments
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Authors and funding
11 authors.
Funding
Abstract
Detection of printed circuit board assembly (PCBA) defects is crucial for improving the efficiency of PCBA manufacturing. This paper proposes PCBA-YOLO, a YOLOv5-based method that can effectively increase the accuracy of PCBA defect detection. First, the spatial pyramid pooling module with cross-stage partial structure is replaced in the neck network of YOLOv5 to capture the resolution features at multiple scales. Second, large kernel convolution is introduced in the backbone network to obtain larger effective receptive fields while reducing computational overhead. Finally, an SIoU loss function that considers the angular cost is utilized to enhance the convergence speed of the model. In addition, an assembled PCBA defect detection dataset named PCBA-DET is created in this paper, containing the corresponding defect categories and annotations of defect locations. The experimental results on the PCB defect dataset demonstrate that the improved method has lower loss values and higher accuracy. Evaluated on the PCBA-DET dataset, the mean average precision reaches 97.3
Identifiers
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.