@article{JIN2026, 
author = {Qingyu JIN and Zhongwen YUE and Xingyuan ZHOU and Jiayao CHEN and Huaqiang LIU},
title = {A hybrid neural network model for intelligent blasthole detection in complex environments},
year = {2026},
journal = {Journal of Mining Science and Technology},
volume = {11},
number = {2},
pages = {265-275},
keywords = {blasthole detection, complex environment, intelligent charging, intelligent tunnel construction, deep learning},
url = {https://www.sciopen.com/article/10.19606/j.cnki.jmst.2025093},
doi = {10.19606/j.cnki.jmst.2025093},
abstract = {To address the difficulty of blasthole detection during the charging phase of drill-and-blast tunnelling, which is aggravated by dust interference and insufficient illumination, this study proposes an intelligent blasthole detection model based on a hybrid neural network. First, a multi-class classification module accurately categorises blasthole images acquired in complex environments; a feature transformation module then converts these images into equivalent ones with a clear background. Subsequently, a dedicated blasthole detection module identifies the blastholes and localises their positions. By strengthening the feature-extraction capability of deformable convolutions, introducing a triple-attention mechanism, and refining the loss function, the model achieves a significant improvement in detection accuracy under adverse conditions. Experimental results demonstrate that, in complex environments, the proposed model attains a detection precision of 94.47 % and a recall of 86.32 %. Compared with state-of-the-art deep-learning object detectors, the proposed model exhibits superior robustness and blasthole detection capability, reliably identifying blasthole locations that conventional models often miss, thereby providing a solid foundation for intelligent charging in tunnelling excavation.}
}