Pengju Ren, Jingyu Wang
This study presents CMF-Net, a lightweight multi-scale feature fusion network designed for the early detection of small fires in coal mines. The primary objective is to improve detection accuracy under challenging environmental conditions characterized by smoke and low illumination. The methodology involves the integration of multi-scale feature extraction and attention mechanisms to enhance detection performance while maintaining low computational complexity. Results indicate that CMF-Net significantly outperforms existing fire detection models, especially for small targets, demonstrating robust detection capabilities in settings where visibility is severely compromised. This advancement facilitates effective early warning systems in underground environments, crucial for improving safety measures against fire hazards.
@article{974ea4cc-45f9-4abf-bd0b-3824bb5d6e35,
title={CMF Net a lightweight multi scale feature fusion n},
author={Pengju Ren and Jingyu Wang},
year={2026},
language={en}
}TY - JOUR TI - CMF Net a lightweight multi scale feature fusion n AU - Pengju Ren AU - Jingyu Wang PY - 2026 LA - en ER -
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