Work

Research on Tunnel Crack Identification Localization and Segmentation Method Based on Improved YOLOX and UNETR++

Year: 2025

Type: article

Abstract: To address the challenges in identifying and segmenting fine irregular cracks in tunnels, this paper proposes a new crack identification, localization and segmentation method based on improved YOLOX and UNETR++. The improved YOLOX recognition algorithm builds upon the original YOLOX network architecture. It replaces the original CSPDarknet backbone with EfficientNet to enhance multi-scale feature extraction while preserving fine texture characteristics of tunnel cracks. By integrating a lightweight ECA module, the proposed method significantly improves sensitivity to subtle crack features, enabling high-precision identification and localization of fine irregular cracks. The UNETR++ segmentation network is adopted to realize efficient and accurate segmentation of fine irregular cracks in tunnels through global feature capture capability and a multi-scale feature fusion mechanism. The experimental results demonstrate that the proposed method achieves integrated processing of crack identification, localization and segmentation, especially for fine and irregular cracks identification and segmentation.

Source: Sensors

DOI: 10.3390/s25113417

Authors: Wei Sun, Xiaohu Liu, Zhiyong Lei

Institutions: Shaanxi University of Technology, Xi'an Technological University

Language: English

Topic: Infrastructure Maintenance and Monitoring

Subfield: Civil and Structural Engineering

Field: Engineering

Domain: Physical Sciences

SDG: Industry, innovation and infrastructure

Open Access status: gold

Estimated APC paid: $3,137