ArticleSensors (Basel, Switzerland)2024
Concrete Surface Crack Detection Algorithm Based on Improved YOLOv8.
Article in Sensors (Basel, Switzerland), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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Who cites it
18 citing papers in PubMed.
- YOLO-Crack: Geometry-Guided Real-Time Crack Detection Framework Toward Edge Deployment.Sensors (Basel, Switzerland) · 2026Article
- Knowledge distillation and pseudo-labeling for lightweight YOLOv11-based structural crack detection.Scientific reports · 2026Article
- Real-Time Small UAV Detection in Complex Airspace Using YOLOv11 with Residual Attention and High-Resolution Feature Enhancement.Journal of imaging · 2026Article
- A Code-Conforming Computer Vision Framework for Visual Inspection of Reinforced and Prestressed Concrete Bridges.Sensors (Basel, Switzerland) · 2026Article
- Adaptive Local-Global Synergistic Perception Network for Hydraulic Concrete Surface Defect Detection.Sensors (Basel, Switzerland) · 2026Article
- A lightweight YOLO11n seg framework for real time surface crack detection with segmentation.Scientific reports · 2026Article
- YOLO11-FR: A bridge crack detection method based on frequency-domain fusion and an edge enhancement mechanism.PloS one · 2026Article
- Crack Detection in Civil Infrastructure Using Autonomous Robotic Systems: A Synergistic Review of Platforms, Cognition, and Autonomous Action.Sensors (Basel, Switzerland) · 2025Review
- Development of an Automated Crack Detection System for Port Quay Walls Using a Small General-Purpose Drone and Orthophotos.Sensors (Basel, Switzerland) · 2025Article
- Building construction crack detection with BCCD YOLO enhanced feature fusion and attention mechanisms.Scientific reports · 2025Article
- Accuracy-Efficiency Trade-Off: Optimizing YOLOv8 for Structural Crack Detection.Sensors (Basel, Switzerland) · 2025Article
- FDSensors (Basel, Switzerland) · 2025Article
- Study on Lightweight Bridge Crack Detection Algorithm Based on YOLO11.Sensors (Basel, Switzerland) · 2025Article
- An Optimized YOLOv11 Framework for the Efficient Multi-Category Defect Detection of Concrete Surface.Sensors (Basel, Switzerland) · 2025Article
- Crack detection in structural images using a hybrid Swin Transformer and enhanced features representation block.Frontiers in artificial intelligence · 2025Article
- LBA-YOLO: A novel lightweight approach for detecting micro-cracks in building structures.PloS one · 2025Article
- Enhance the Concrete Crack Classification Based on a Novel Multi-Stage YOLOV10-ViT Framework.Sensors (Basel, Switzerland) · 2024Article
- Dmg2Former-AR: Vision Transformers with Adaptive Rescaling for High-Resolution Structural Visual Inspection.Sensors (Basel, Switzerland) · 2024Article
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3 authors.
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Abstract
Concrete surface crack detection is a critical research area for ensuring the safety of infrastructure, such as bridges, tunnels and nuclear power plants, and facilitating timely structural damage repair. Addressing issues in existing methods, such as high cost, lengthy processing times, low efficiency, poor effectiveness and difficulty in application on mobile terminals, this paper proposes an improved lightweight concrete surface crack detection algorithm, YOLOv8-Crack Detection (YOLOv8-CD), based on an improved YOLOv8. The algorithm integrates the strengths of visual attention networks (VANs) and Large Convolutional Attention (LCA) modules, introducing a Large Separable Kernel Attention (LSKA) module for extracting concrete surface crack and local feature information, adapted for features such as fracture susceptibility, large spans and slender shapes, thereby effectively emphasizing crack shapes. The Ghost module in the YOLOv8 backbone efficiently extracts essential information from original features at a minimal cost, enhancing feature extraction capability. Moreover, replacing the original convolution structure with GSConv in the neck network and employing the VoV-GSCSP module adapted for the YOLOv8 framework reduces floating-point operations during feature channel fusion, thereby lowering computational complexity whilst maintaining model accuracy. Experimental results on the RDD2022 and Wall Crack datasets demonstrate the improved algorithm increases in mAP50 by 15.2% and 12.3%, respectively, and in mAP50-95 by 22.7% and 17.2%, respectively, whilst achieving a reduced model computational load of only 7.9 × 10
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