ArticleSensors (Basel, Switzerland)2023
Lightweight Network DCR-YOLO for Surface Defect Detection on Printed Circuit Boards.
Article in Sensors (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
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The trial behind it
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Who cites it
2 citing papers in PubMed.
- Focal-HAIN: a lightweight model with adaptive modulation and hierarchical interaction for real-time crop pest and disease monitoring.Frontiers in plant science · 2026Article
- PCB-YOLO: Enhancing PCB surface defect detection with coordinate attention and multi-scale feature fusion.PloS one · 2025Article
Corrections and comments
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Authors and funding
3 authors.
Funding
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Abstract
To resolve the problems associated with the small target presented by printed circuit board surface defects and the low detection accuracy of these defects, the printed circuit board surface-defect detection network DCR-YOLO is designed to meet the premise of real-time detection speed and effectively improve the detection accuracy. Firstly, the backbone feature extraction network DCR-backbone, which consists of two CR residual blocks and one common residual block, is used for small-target defect extraction on printed circuit boards. Secondly, the SDDT-FPN feature fusion module is responsible for the fusion of high-level features to low-level features while enhancing feature fusion for the feature fusion layer, where the small-target prediction head YOLO Head-P3 is located, to further enhance the low-level feature representation. The PCR module enhances the feature fusion mechanism between the backbone feature extraction network and the SDDT-FPN feature fusion module at different scales of feature layers. The C
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Registered trials
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