A Novel YOLO Algorithm Integrating Attention Mechanisms and Fuzzy Information for Pavement Crack Detection

Abstract

Pavement crack detection is widely spread over road maintenance, ensuring the longevity and safety of infrastructure. Traditional manual inspection methods are time-consuming, labor-intensive, and prone to errors. In response, automated crack detection systems based on deep learning have emerged, offering more efficient and accurate solutions. However, existing models often face challenges such as large model sizes, slow inference speeds, and limited applicability in real-time applications. In this paper, we propose a novel light-weight Crack Regional Segmentation method based on YOLOv11, which introduces attention mechanisms to address challenges in pavement images, such as varying crack sizes, occlusion, and irregular surface textures. By embedding a region-based attention mechanism into the YOLOv11 network, the method enhances the model’s ability to focus on crack features. Specifically, the model network layers are progressively pruned to reduce the number of parameters and floating-point operations, thereby further improving operational efficiency and refining detection in the target regions. Furthermore, to tackle issues with blurred or indistinct crack boundaries, we present a fuzzy information-guided YOLOv11-based model, FIG-YOLO. This model integrates fuzzy logic and fuzzy membership functions to handle uncertainty in crack detection. The fuzzy membership functions are used to quantify the degree of crack features, allowing the model to better distinguish between crack and non-crack regions, especially in cases where crack boundaries are unclear. This approach significantly improves the accuracy of crack detection and segmentation. Extensive experiments demonstrate that our approach effectively addresses challenges such as complex backgrounds and blurred crack edges in pavement images. This research not only provides a novel solution for the automated detection of pavement cracks but also offers insights into the development of intelligent road maintenance systems. With the expansion of large-scale datasets and the advancement of deep learning models, pavement crack detection algorithms are expected to further enhance their accuracy and efficiency, offering significant support for road infrastructure management.