Personnel detection in a coal mine is of great significance for monitoring the safety and real-time positioning of underground personnel. However, the uneven illumination, large dust, imaging noise, and other factors give the existing methods a high false detection rate for personnel detection. To overcome this problem, an improved YOLOv4 network for personnel detection based on thermal infrared images is proposed. First, the infrared personnel images are preprocessed by the method of super-resolution convolutional neural network to enhance their resolutions. Second, to improve the detection performance of the YOLOV4 network, the K-means++ clustering is used to optimize the anchor boxes estimation before the network training. Third, to further strengthen the generalization ability of the YOLOv4 and reduce the complexity of network training, the strategy of transfer learning is applied to train the YOLOv4 network. Experimental results show that on the infrared personnel image dataset underground coal mine, the improved YOLOv4 model achieves 96.25% and 48.2 fps in global AP and detection speed, respectively. Moreover, the improved YOLOv4 model has the advantages of strong robustness and generalization ability, which is very suitable for underground personnel detection, and provides a strong guarantee for the safety monitoring and management of coal mine personnel.

A novel enhancement algorithm for degraded image using dual-domain-adaptive wavelet and improved fuzzy transform is proposed, aiming at the problem of surveillance videos degradation caused by the complex lighting conditions underground coal mine. Firstly, the dual-domain filtering (DDF) is used to decompose the image into base image and detail image, and the contrast limited adaptive histogram enhancement (CLAHE) is adopted to adjust the overall brightness and contrast of the base image. Then, the discrete wavelet transform (DWT) is utilized to obtain the low frequency sub-band (LFS) and high frequency sub-band (HFS). Next, the wavelet shrinkage threshold is applied to calculate the wavelet threshold corresponding to the HFS at different scales. Meanwhile, a new Garrate threshold function that introduces adjustment factor and enhancement coefficient is designed to adaptively de-noise and enhance the HFS coefficients, and the Gamma function is employed to correct the LFS coefficients. Finally, the PAL fuzzy enhancement operator is improved and used to perform contrast enhancement and highlight area suppression on the reconstructed image to obtain an enhanced image. Experimental results show that the proposed algorithm can not only significantly improve the overall brightness and contrast of the degraded image but also suppresses the noise of dust and spray and enhances the image details. Compared with the similar algorithms of STFE, GTFE, CLAHE, SSR, MSR, DGR, and MSWT algorithms, the indicator values of comprehensive performance of the proposed algorithm are increased by 205, 195, 200, 185, 185, 85, 140, and 215, respectively. Moreover, compared with the other seven algorithms, the proposed algorithm has strong robustness and is more suitable for image enhancement in different mine environments.

With the rapid development of Internet of things (IoT) and computer vision (CV), the application of combining the IoT platform and CV technology to monitor the worker safety has attracted more and more attention in the field of industrial information. Worker identification is a prerequisite for safety management in industrial production, and safety helmet can not only protect worker’s head from accidental injuries but also help to identify the work types of workers through different colors. Therefore, this study proposes an intelligent method for worker identification based on moving personnel detection and helmet color characteristics. First, the motion objects that contain personnel and nonpersonnel are detected by the Gaussian mixture model (GMM) and extracted to generate the region of interest (RoI) images. Then, the multiple‐scale histogram of oriented gradient (MHOG) features of the RoI images are extracted, and the personnel images are identified by the support vector machine (SVM). Third, the workers’ head images are obtained by the OpenPose model and personnel mask, and the GoogLeNet‐based transfer learning network is established to extract the head images features and realize worker identification. This method is tested on our dataset, and the average accuracy of worker identification for multiple helmet color combinations reaches 99.43%, which is robust to workers’ angle, scale, and occlusion.

Methane telemetry plays a vital role in ensuring the safe production of coal mines and monitoring the leakage of natural gas pipelines. Harmonic detection is the key technology of methane telemetry accuracy and sensitivity, but the current telemetry distance is short, the relationship between different modulation parameters is complex, and the harmonic signal is affected by noise interference. These factors seriously affect the development of harmonic detection technology. In this paper, the principle of methane telemetry based on harmonic detection technology is introduced. The present situation and characteristics of harmonic detection technology are expounded. The problems existing in harmonic detection are analyzed. Finally, the future development trend is discussed.

The underground mine environment is dangerous and harsh, tracking and detecting humans based on computer vision is of great significance for mine safety monitoring, which will also greatly facilitate identification of humans using the symmetrical image features of human organs. However, existing methods have difficulty solving the problems of accurate identification of humans and background, unstable human appearance characteristics, and humans occluded or lost. For these reasons, an improved aberrance repressed correlation filter (IARCF) tracker for human tracking in underground mines based on infrared videos is proposed. Firstly, the preprocess operations of edge sharpening, contrast adjustment, and denoising are used to enhance the image features of original videos. Secondly, the response map characteristics of peak shape and peak to side lobe ratio (PSLR) are analyzed to identify abnormal human locations in each frame, and the method of calculating the image similarity by generating virtual tracking boxes is used to accurately relocate the human. Finally, using the value of PSLR and the highest peak point of the response map, the appearance model is adaptively updated to further improve the robustness of the tracker. Experimental results show that the average precision and success rate of the IARCF tracker in the five underground scenarios reach 0.8985 and 0.7183, respectively, and the improvement of human tracking in difficult scenes is excellent. The IARCF tracker can effectively track underground human targets, especially occluded humans in complex scenes.