ArticleSensors (Basel, Switzerland)2025
Accuracy-Efficiency Trade-Off: Optimizing YOLOv8 for Structural Crack Detection.
Article in Sensors (Basel, Switzerland), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- Architecture and hyperparameter optimization of YOLOv5 and YOLOv8 for weed detection in garlic fields.Scientific reports · 2026Article
- A lightweight YOLO11n seg framework for real time surface crack detection with segmentation.Scientific reports · 2026Article
- Enhanced YOLO-based framework for accurate detection and identification of common wheat impurities with distinct objects.Scientific reports · 2025Article
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Authors and funding
3 authors.
Funding
Abstract
To address the accuracy-efficiency trade-off faced by deep learning models in structural crack detection, this paper proposes an optimized version of the YOLOv8 model. YOLO (You Only Look Once) is a real-time object detection algorithm known for its high speed and decent accuracy. To improve crack feature representation, the backbone is enhanced with the SimAM attention mechanism. A lightweight C3Ghost module reduces parameter count and computation, while a bidirectional multi-scale feature fusion structure replaces the standard neck to enhance efficiency. Experimental results show that the proposed model achieves a mean Average Precision (mAP) of 88.7% at 0.5 IoU and 69.4% for mAP@0.5:0.95, with 12.3% fewer Giga Floating Point Operations (GFlops), and faster inference. These improvements significantly enhance the detection of fine cracks while maintaining real-time performance, making it suitable for engineering scenarios.
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