The belt conveyor is an essential piece of equipment in coal mining for coal transportation, and its stable operation is key to efficient production. Belt surface of the conveyor is vulnerable to foreign bodies which can be extremely destructive. In the past decades, much research and numerous approaches to inspect belt status have been proposed, and machine learning-based non-destructive testing (NDT) methods are becoming more and more popular. Deep learning (DL), as a branch of machine learning (ML), has been widely applied in data mining, natural language processing, pattern recognition, image processing, etc. Generative adversarial networks (GAN) are one of the deep learning methods based on generative models and have been proved to be of great potential. In this paper, a novel multi-classification conditional CycleGAN (MCC-CycleGAN) method is proposed to generate and discriminate surface images of damages of conveyor belt. A novel architecture of improved CycleGAN is designed to enhance the classification performance using a limited capacity images dataset. Experimental results show that the proposed deep learning network can generate realistic belt surface images with defects and efficiently classify different damaged images of the conveyor belt surface.

The belt conveyor is the most commonly used conveying equipment in the coal mining industry. As the core part of the conveyor, the belt is vulnerable to various failures, such as scratches, cracks, wear and tear. Inspection and defect detection is essential for conveyor belts, both in academic research and industrial applications. In this paper, we discuss existing techniques used in industrial production and state-of-the-art theories for conveyor belt tear detection. First, the basic structure of conveyor belts is discussed and an overview of tear defect detection methods for conveyor belts is studied. Next, the causes of conveyor belt tear are classified, such as belt aging, scratches by sharp objects, abnormal load or a combination of the above reasons. Then, recent mainstream techniques and theories for conveyor belt tear detection are reviewed, and their characteristics, advantages and shortcomings are discussed. Furthermore, image dataset preparation and data imbalance problems are studied for belt defect detection. Moreover, the current challenges and opportunities for conveyor belt defect detection are discussed. Lastly, a case study was carried out to compare the detection performance of popular techniques using industrial image datasets. This paper provides professional guidelines and promising research directions for researchers and engineers based on the leading theories in machine vision and deep learning.

In this paper, we propose a simple but effective method to remove haze from a single input image. This method is not only prior on dark channel, but also light. Based on dark channel prior (DCP), a new haze removal scheme is proposed by Dr. He in [6] and it is becoming popular because of its dark channel statistics of outdoor haze-free images. Whereas, there are two problems in the scheme proposed in [6]: the cost of computing the transmission map using soft mapping is high and the atmospheric light is over-exposure when a bright area is shown in images. This paper proposes a novel dehazing algorithm with dark channel and light channel where the light channel is a kind of statistics of outdoor hazy images. Moreover, the guided filter is introduced to refine the dark channel and the light channel in this paper. The objective of proposed dehazing algorithm (PDA) is to alleviate or remove these problems found in [6]. To verify the PDA and compare it with the DCP scheme, several examples are given in this paper. The results show that the PDA is about 25 times faster than the DCP since the soft matting is avoided on average, and visual quality in the PDA without over-exposure problem is better than that in the DCP. With these improvements, the proposed method may be applied in video surveillance, intelligent transportation system and remote sensing.

Due to the poor lighting conditions and the presence of a large amount of suspended dust in coal mines, obtained video has problems with uneven lighting and low differentiation of facial features. In order to address these problems, an improved image enhancement method is proposed. Firstly, the characteristics of underground coal mine images are analyzed, and median filtering is selected for noise removal. Then, the gamma function and fractional order operator are introduced, and an image enhancement algorithm based on particle swarm optimization is proposed. Finally, several experiments are conducted, and the results show that the proposed improved algorithm outperforms classical image enhancement algorithms, such as MSR, CLAHE and HF. Compared with the original image, the evaluation metrics of the enhanced Yale face images, including average local standard deviation, average gradient, information entropy and contrast, are improved by 113.1%, 63.8%, 22.8% and 24.1%, respectively. Moreover, the proposed algorithm achieves a superior enhancement effect in the simulated coal mine environment.

A complex stratum formed due to the influence of internal and external dynamic geological processes will lead to extremely complex mining conditions in deep exploration and development of oil, gas, coal and other resources, processes mainly threatened by disasters such as coal and gas conflict, mine water inrush, and rock burst. Combined with formation identification and measurement while drilling technology, the drilling level of underground drilling robot in coal mines is constantly developing. In order to prevent coal mine accidents and achieve safe and efficient mining, efficient and accurate drilling is the key, and should be based on research on the influence of complex stratum on the drilling trajectory. In order to comprehensively and systematically summarize the research on the influence of a complex stratum on drilling tool mechanics, this paper describes the history and current situation of complex stratum exploration, measurement while drilling technology, borehole bending conditions, stress analysis of complex coal seams on drilling tools, formation force theory and method, and geosteering drilling technology. In addition, the research and application of directional drilling technology in gas control, water hazard prevention and geological anomaly detection are also discussed.