A lightweight and attention-enhanced framework for robust pavement defect detection

Accurately detecting pavement anomalies, a critical task within structural health monitoring (SHM), is essential for infrastructure safety and automated monitoring systems. However, existing deep learning based object detectors, including state-of-the-art You Only Look Once (YOLO) variants, often struggle with defects such as elongated and low-contrast potholes due to irregular geometry and limited spatial context awareness. In this study, we propose an Efficient and CA enhanced You Only Look Once framework (EC-YOLO), an improved deep learning based object detection network designed to address these challenges. The proposed model builds upon the YOLOv11 architecture and introduces two major enhancements: (1) replacing the shallow backbone with EfficientNet-B0 for superior fine-grained feature extraction, and (2) integrating a Coordinate Attention (CA) module into the large-object detection head to capture long-range spatial dependencies. Extensive experiments on the Urban Digital Twins dataset demonstrate that EC-YOLO achieves state-of-the-art performance, attaining 96.5% mean Average Precision (mAP)@0.5 and 71.3% [email protected]:0.95. After deployment engine optimization, the model maintains real-time inference at 225.4 frames per second (FPS) on an NVIDIA Jetson Orin Nano with only 1.7 giga floating point operations (GFLOPs). Ablation studies further verify the contribution of each component. Moreover, EC-YOLO exhibits strong generalization by outperforming existing models on the Urban Digital Twins for Intelligent Road Inspection (UDTIRI) external benchmark. Overall, deployment verification on the Jetson platform confirms that EC-YOLO is a robust, lightweight, and effective solution for practical road defect inspection in resource-constrained environments.