A Transformer-Based Low-Light Enhancement Algorithm for Rock Bolt Detection in Low-Light Underground Mine Environments
Year: 2025
Type: article
Abstract: Underground roadway support is a critical component for ensuring safety in mining operations. In recent years, with the rapid advancement of intelligent technologies, computer vision-based automatic rock bolt detection methods have emerged as a promising alternative to traditional manual inspection. However, the underground mining environment inherently suffers from severely insufficient lighting. Images captured on-site often exhibit problems such as low overall brightness, blurred local details, and severe color distortion. To address the problem, this study proposed a novel low-light image enhancement algorithm, PromptHDR. Based on Transformer architecture, the algorithm effectively suppresses color distortion caused by non-uniform illumination through a Lighting Extraction Module, while simultaneously introducing a Prompt block incorporating a Mamba mechanism to enhance the model’s contextual understanding of the roadway scene and its ability to preserve rock bolt details. Quantitative results demonstrate that the PromptHDR algorithm achieves Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity (SSIM) index scores of 24.19 dB and 0.839, respectively. Furthermore, the enhanced images exhibit more natural visual appearance, adequate brightness recovery, and well-preserved detailed information, establishing a reliable visual foundation for the accurate identification of rock bolts.
Source: Processes
DOI: 10.3390/pr13123914
Authors: Wenzhen Yan, Fuming Qu, Yingzhen Wang, Jiajun Xu, Jiapan Li, Lingyu Zhao
Institution: University of Science and Technology Beijing
Language: English
Topic: Image Enhancement Techniques
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $2,896
OpenAlex ID: W4416958476