ArticleScientific reports2023
Metal surface defect detection based on improved YOLOv5.
Article in Scientific reports, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- Deep learning enabled intelligent robotic system for aeroengine blade surface inspection.Scientific reports · 2026Article
- MSFE-YOLO: A Steel Surface Defect Detection Algorithm Integrating Multi-Scale Frequency Domain and Defect-Aware Attention.Sensors (Basel, Switzerland) · 2026Article
- Automated Detection of Quality Deviations in Poultry Processing Using Step-Specific YOLOv12 Models.Foods (Basel, Switzerland) · 2026Article
- Few-shot cross-episode adaptive memory for metal surface defect semantic segmentation.Scientific reports · 2026Article
- MS-YOLOv11: A multi-scale feature fusion network for real-time rooftop photovoltaic detection from UAV images.PloS one · 2026Article
- Improved faster R-CNN for steel surface defect detection in industrial quality control.Scientific reports · 2025Article
- Efficient deep learning based rail fastener screw detection method for fastener screw maintenance robot under complex lighting conditions.Scientific reports · 2024Article
- RJ-TinyViT: an efficient vision transformer for red jujube defect classification.Scientific reports · 2024Article
- Enhanced photovoltaic panel defect detection via adaptive complementary fusion in YOLO-ACF.Scientific reports · 2024Article
- Defect detection of printed circuit board assembly based on YOLOv5.Scientific reports · 2024Article
- Surface defect detection of hot rolled steel based on multi-scale feature fusion and attention mechanism residual block.Scientific reports · 2024Article
Corrections and comments
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Authors and funding
8 authors.
Funding
Abstract
During the production of metal material, various complex defects may come into being on the surface, together with large amount of background texture information, causing false or missing detection in the process of small defect detection. To resolve those problems, this paper introduces a new model which combines the advantages of CSPlayer module and Global Attention Enhancement Mechanism based on the YOLOv5s model. First of all, we replace C3 module with CSPlayer module to augment the neural network model, so as to improve its flexibility and adaptability. Then, we introduce the Global Attention Mechanism (GAM) and build the generalized additive model. In the meanwhile, the attention weights of all dimensions are weighted and averaged as output to promote the detection speed and accuracy. The results of the experiment in which the GC10-DET augmented dataset is involved, show that the improved algorithm model performs better than YOLOv5s in precision, mAP@0.5 and mAP@0.5: 0.95 by 5.3%, 1.4% and 1.7% respectively, and it also has a higher reasoning speed.
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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.