Enhancing Road Maintenance Through Cyber-Physical Integration: The LEE-YOLO Model for Drone-Assisted Pavement Crack Detection

Drones for road crack detection provide a real-time Cyber-Physical System (CPS) solution, improving road surface assessment for efficient highway maintenance. CPS integrates computational algorithms with physical components, offering advantages over traditional manual inspections, which are slow and prone to false positives, especially in complex environments. To address these challenges, this paper presents the LEE-YOLO model, a novel and lightweight solution that leverages drones within a CPS framework to enable efficient road crack detection. The model incorporates a streamlined network built upon the YOLOv8n model and introduces an innovative lightweight fusion structure, SGC2f, which uses grouped convolutions and linear operations to reduce model weight and complexity markedly. Additionally, the proposed Efficient Bidirectional Feature Pyramid Network (EBiFPN) optimizes output channel utilization within the feature network, enhancing the model’s capacity to detect targets at multiple scales. Furthermore, the Efficient Multi-Scale Attention (EMA) module in the model’s backbone layer is designed to reduce redundant data and improve the extraction of relevant features. Experimental results demonstrate that the LEE-YOLO model outperforms YOLOv8n, achieving a 4.3% higher precision and a 4.0% improvement in mean average precision (mAP). Importantly, it achieves a 43.5% reduction in weight and a 39.5% decrease in computational demand, effectively balancing performance and efficiency. These advancements significantly enhance the potential for drone applications in highway maintenance and detection within the Cyber-Physical Systems domain.