Year: 2025
Type: article
Abstract: The accurate detection and segmentation of concrete cracks are crucial for maintaining the integrity and safety of infrastructure. Traditional manual inspection methods are often constrained by background complexity and environmental noise, while deep learning models face challenges related to data dependency and poor generalization in complex scenarios. To address these issues, this paper proposes an enhanced transFissNet model that integrates a ResNet101 backbone with Transformer modules for self-attention and multi-scale feature extraction. The model improves the robustness of crack detection under varying lighting conditions and irregular crack morphologies. Experimental results on multiple benchmark datasets demonstrate that transFissNet achieves an accuracy of 96.8%, outperforming existing mainstream methods. The proposed approach provides a reliable and scalable solution for automated crack segmentation and contributes to the advancement of intelligent structural health monitoring.
Source: Electronics
Authors: Tianhao Ye, Min He, Yexuan Wang, Jiaying Wang, Lei Zhu, Jie Zhang
Institution: Xi'an University of Technology
Language: English
Subfield: Civil and Structural Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $2,896
Awards: G2670902233, G932034209
OpenAlex ID: W4409729545