Underground coal mine personnel detection method and device based on deep learning

By using deep learning technology to perform spatiotemporal alignment and adaptive fusion of multimodal images from underground coal mines, enhanced features are generated, and the detection model and dynamic threshold evaluation are optimized. This solves the problem of high false detection rate of traditional detection methods in underground coal mines and achieves high-precision, low-latency personnel detection.

CN120877332APending Publication Date: 2025-10-31陕西小保当矿业有限公司
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Patent Information

Application Number
CN202511060632.1
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2025-07-30
Publication Date
2025-10-31

AI Technical Summary

Technical Problem

Traditional manual inspections or fixed sensor monitoring methods have blind spots and slow response times in underground coal mines. Furthermore, detection methods based on thresholds or template matching have a high false detection rate, making it difficult to meet the needs of dynamic safety management underground.

Method used

A deep learning-based approach is adopted to acquire multimodal environmental images through intelligent sensors, perform spatiotemporal alignment preprocessing, adaptively fuse visible light and infrared light features, extract the spatiotemporal context of personnel movement underground, generate enhanced features, update the detection model, and optimize the detection results by combining neighborhood targets and dynamic threshold evaluation.

Benefits of technology

It achieves high-precision, low-latency personnel detection in complex downhole environments, significantly reducing the false negative rate and meeting the real-time and robust requirements of intelligent monitoring.

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Abstract

The invention relates to the technical field of image detection, and provides a coal mine underground personnel detection method and device based on deep learning. The method comprises the following steps: acquiring a multi-modal environment image from an underground coal mine through a preset intelligent sensor, preprocessing the environment image, and generating preprocessing features of time-space alignment; on the basis of visible light features and red network light features in the preprocessed features, adaptive fusion based on modal weight is carried out to generate fusion features, and spatio-temporal context relationships of underground personnel movement are extracted from the fusion features to generate enhanced features; updating the detection model based on the enhanced features, and generating a target set containing personnel detection results; determining a detection score corresponding to each target based on a neighborhood target corresponding to the target set; and performing dynamic threshold adaptive evaluation based on the detection score, and determining personnel information. High-precision and low-delay personnel detection is realized in an underground changeable environment, the real-time and robustness requirements of intelligent monitoring are met, and the omission ratio is remarkably reduced compared with that of a traditional method.
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