Enhancing Crack Segmentation Network with Multiple Selective Fusion Mechanisms

Automated crack detection is vital for structural maintenance in areas such as construction, roads, and bridges. Accurate crack detection allows for the timely identification and repair of cracks, reducing safety risks and extending the service life of structures. However, traditional methods struggle with fine cracks, complex backgrounds, and image noise. In recent years, although deep learning techniques excel in pixel-level crack segmentation, challenges like inadequate local feature processing, information loss, and class imbalance persist. To address these challenges, we propose an encoder–decoder network based on multiple selective fusion mechanisms. Initially, a star feature enhancement module is designed to resolve the issues of insufficient local feature processing and feature redundancy during the feature extraction process. Then, a multi-scale adaptive fusion module is developed to selective capture both global and local contextual information, mitigating the information loss. Finally, to tackle class imbalance, a multi-scale monitoring and selective output module is introduced to enhance the model’s focus on crack features and suppress the interference from background and irrelevant information. Extensive experiments are conducted on three publicly available crack datasets: SCD, CFD, and DeepCrack. The results demonstrate that the proposed segmentation network achieves superior performance in pixel-level crack segmentation, with Dice scores of 66.2%, 54.2%, and 86.8% and mIoU values of 74.4%, 67.5%, and 87.9% on the SCD, CFD, and DeepCrack datasets, respectively. These results outperform those of existing models, such as U-Net, DeepLabv3+, and Attention UNet, particularly in handling complex backgrounds, fine cracks, and low-contrast images. Furthermore, the proposed MSF-CrackNet also significantly reduces computational complexity, with only 2.39 million parameters and 8.58 GFLOPs, making it a practical and efficient solution for real-world crack detection tasks, especially in scenarios with limited computational resources.