ArticleScientific reports2024
Surface defect detection of hot rolled steel based on multi-scale feature fusion and attention mechanism residual block.
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 15 papers.
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Who cites it
15 citing papers in PubMed.
- CCP-YOLO: An Improved YOLOv11n Algorithm for Steel Surface Defect Detection.Sensors (Basel, Switzerland) · 2026Article
- PPFS-YOLO: Physics-Prior Frequency-Spatial Fusion for Robust Container Surface Damage Detection.Sensors (Basel, Switzerland) · 2026Article
- MSFE-YOLO: A Steel Surface Defect Detection Algorithm Integrating Multi-Scale Frequency Domain and Defect-Aware Attention.Sensors (Basel, Switzerland) · 2026Article
- A lightweight metallographic defect detection framework via structured priors and energy-aware attention under strong grain boundary interference.Scientific reports · 2026Article
- Model-Centric or Data-Centric Approach? A Case Study on the Classification of Surface Defects in Steel Hot Rolling Using Convolutional Neural Networks.Sensors (Basel, Switzerland) · 2026Article
- Contrastive learning enhanced MobileMamba for real time industrial defect detection on edge devices.Scientific reports · 2026Article
- Article
- EMamba: A MoE-enhanced state space network for robust steel surface defect detection.PloS one · 2026Article
- An improved steel defect detection model using multi-scale feature fusion based on YOLO-MFD.Scientific reports · 2025Article
- MCH-YOLOv12: Research on Surface Defect Detection Algorithm for Aluminum Profiles Based on Improved YOLOv12.Sensors (Basel, Switzerland) · 2025Article
- Real-time classroom student behavior detection based on improved YOLOv8s.Scientific reports · 2025Article
- Metal surface defect detection using SLF-YOLO enhanced YOLOv8 model.Scientific reports · 2025Article
- Feature optimization-guided high-precision and real-time metal surface defect detection network.Scientific reports · 2024Article
- Enhanced photovoltaic panel defect detection via adaptive complementary fusion in YOLO-ACF.Scientific reports · 2024Article
- Target Detection of Diamond Nanostructures Based on Improved YOLOv8 Modeling.Nanomaterials (Basel, Switzerland) · 2024Article
Corrections and comments
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Authors and funding
8 authors.
Funding
Abstract
To improve the precision of defect categorization and localization in images, this paper proposes an approach for detecting surface defects in hot-rolled steel strips. The approach uses an improved YOLOv5 network model to overcome the issues of inadequate feature extraction capacity and suboptimal feature integration when identifying surface defects on steel strips. The proposed method achieves higher detection accuracy and localization precision, making it more competitive and applicable in real production. Firstly, the multi-scale feature fusion (MSF) strategy is utilized to fuse shallow and deep features effectively and enrich detailed information relevant to target defects. Secondly, the CSPLayer Res2Attention block (CRA block) residual module is introduced to reduce the loss of defect information during hierarchical transmission, thereby enhancing the extraction of fine-grained features and improving the perception of details and global features. Finally, the experimental results indicate that the mAP on the NEU-DET and GC10-DET datasets approaches 78.5% and 67.3%, respectively, which is 4.9% and 2.1% higher than that of the baseline. Meanwhile, it has higher precision and more precise localization capabilities than other methods. Furthermore, it also achieves 59.2% mAP on the APDDD dataset, indicating its potential for growth in further domains.
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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.