A lightweight and high-precision road defect detection method based on improved YOLO11

Abstract

This paper proposes an improved lightweight YOLO11s algorithm to address the issues of excessive model complexity and poor performance when detecting small objects in road defect detection. By incorporating the StarNet architecture to reconstruct the backbone network, the method utilizes star-shaped operations to enhance fine-grained feature extraction while reducing computational overhead. It integrates the parameter-free attention mechanism SimAM, which employs three-dimensional weighted inference to enhance target responses and suppress background noise. Additionally, the Shape-IoU loss function is introduced to improve the regression accuracy of irregularly shaped objects through dynamic shape constraints. The experimental results demonstrate that the proposed algorithm attained an mAP50 of 87.02% on the RDD2022 dataset, indicating a 4.33% increase in recall. Concurrently, it achieved a 52.7% and 50.7% reduction in parameters and computational cost, respectively, thereby demonstrating a judicious balance between detection accuracy and lightweight deployment.