Article: Deep learning-driven multimodal early warning analysis for intelligent security in coal mine camps Journal: International Journal of Information and Communication Technology (IJICT) 2026 Vol.27 No.60 pp.1 - 20 Abstract: Coal mine safety is crucial for both life and production. However, traditional monitoring relies on a single sensor, resulting in a high rate of missed alarms in complex underground environments. To address multiple challenges such as changes in light, dust interference, etc. this study proposes a deep learning early warning system that integrates video, infrared, and vibration data. Through cross-modal feature fusion and multi-task learning, it achieves collaborative perception of abnormal human behaviours and equipment failures. Experimental results show that the system achieves an area under the curve of 0.982 for abnormal behaviour detection on public datasets, which is approximately 7% higher than that of a single visual model; the accuracy of fire warning reaches 96.7%, and the false alarm rate is reduced by 5.3%. This method provides a highly reliable and scalable technical path for intelligent safety monitoring in coal mines around the clock. Inderscience Publishers - linking academia, business and industry through research

Title: Deep learning-driven multimodal early warning analysis for intelligent security in coal mine camps

Authors: Chaoyi Zhou; Lanfeng Zhang; Huiwei Wang

Addresses: Shendong Coal Intelligent Technology Center, Yulin, 719315, China ' Shaanxi Yijiexin Information Technology Co., Ltd., Xi'an, 710065, China ' Shendong Coal Intelligent Technology Center, Yulin, 719315, China

Abstract: Coal mine safety is crucial for both life and production. However, traditional monitoring relies on a single sensor, resulting in a high rate of missed alarms in complex underground environments. To address multiple challenges such as changes in light, dust interference, etc. this study proposes a deep learning early warning system that integrates video, infrared, and vibration data. Through cross-modal feature fusion and multi-task learning, it achieves collaborative perception of abnormal human behaviours and equipment failures. Experimental results show that the system achieves an area under the curve of 0.982 for abnormal behaviour detection on public datasets, which is approximately 7% higher than that of a single visual model; the accuracy of fire warning reaches 96.7%, and the false alarm rate is reduced by 5.3%. This method provides a highly reliable and scalable technical path for intelligent safety monitoring in coal mines around the clock.

Keywords: coal mine security; multi-modal fusion; anomaly detection; intelligent early warning.

DOI: 10.1504/IJICT.2026.153938

International Journal of Information and Communication Technology, 2026 Vol.27 No.60, pp.1 - 20

Received: 05 Feb 2026
Accepted: 08 Mar 2026
Published online: 08 Jun 2026 *

Title: Deep learning-driven multimodal early warning analysis for intelligent security in coal mine camps

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