Abstract
Accurate and real-time assessment of road infrastructure is critical for smart city maintenance and transportation safety. However, conventional object detection models often struggle with complex environmental factors, such as varying illumination, shadows, and background noise, leading to false detections and missed fine-grained defects. In this study, we propose YOLO-CBAM, a lightweight and fast neural network architecture tailored for real-time road surface damage detection. The standard YOLO11s backbone is enhanced through the integration of a Convolutional Block Attention Module (CBAM), which synergistically applies channel and spatial attention mechanisms. This modification enables the network to actively suppress irrelevant background visual noise and focus exclusively on structural defects like longitudinal cracks and potholes. Extensive experiments conducted on a comprehensive dataset reveal that the implementation of partial transfer learning significantly mitigates early-stage gradient shock, allowing the model to achieve a mean Average Precision (mAP@50) of 0.60 in just 40 training epochs. Deployed on an NVIDIA RTX 4070 Ti, the proposed framework achieves an inference speed of 25 frames per second (FPS), demonstrating an optimal balance between detection accuracy and computational efficiency. The YOLO-CBAM model provides a robust, cost-effective solution for automated video surveillance and road condition monitoring in smart city infrastructures.