ArticleScientific reports2025
Metal surface defect detection using SLF-YOLO enhanced YOLOv8 model.
Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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10 citing papers in PubMed.
- A low-cost "plant-scanner" platform for automated detection of Ustilago maydis infection in maize using deep learning.Scientific reports · 2026Article
- Robust cross-domain blast furnace tuyere leak detection via few-shot guided simulation-to-real adaptation.Scientific reports · 2026Article
- DAER-YOLO: Defect-Aware and Edge-Reconstruction Enhanced YOLO for Surface Defect Detection of Varistors.Journal of imaging · 2026Article
- An Automated Vision-Based Inspection System for Metallic Lock Surface Defects Using a Transformer-Enhanced U-Net.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
- Few-shot cross-episode adaptive memory for metal surface defect semantic segmentation.Scientific reports · 2026Article
- Electroencephalogram-based multimodal attention level classification using deep learning techniques.Frontiers in human neuroscience · 2026Article
- YOLO-BP for detection of multi-scale and high intra-class variation electrode cap defects in resistance spot welding.PloS one · 2026Article
- Development and Implementation of a Defect Detection Model for Microstructures Using Image Processing Methods.Materials (Basel, Switzerland) · 2025Article
- Concrete slump detection based on light-AWGAM-YOLOv8n.Scientific reports · 2025Article
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5 authors.
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Abstract
This paper addresses the industrial demand for precision and efficiency in metal surface defect detection by proposing SLF-YOLO, a lightweight object detection model designed for resource-constrained environments. The key innovations of SLF-YOLO include a novel SC_C2f module with a channel gating mechanism to enhance feature representation and regulate information flow, and a newly designed Light-SSF_Neck structure to improve multi-scale feature fusion and morphological feature extraction. Additionally, an improved FIMetal-IoU loss function is introduced to boost generalization performance, particularly for fine-grained and small-target defects. Experimental results demonstrate that SLF-YOLO achieves a mean Average Precision (mAP) of 80.0% on the NEU-DET dataset, outperforming YOLOv8's 75.9%. On the AL10-DET dataset, SLF-YOLO achieves a mAP of 86.8%, striking an effective balance between detection accuracy and computational efficiency without increasing model complexity. Compared to other mainstream models, SLF-YOLO demonstrates strong detection accuracy while maintaining a lightweight architecture, making it highly suitable for industrial applications in metal surface defect detection. The source code is available at https://github.com/zacianfans/SLF-YOLO .
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