Conveyor is one of the main equipment for coal production and transportation. Due to impact fatigue, uneven surface stress of conveyor belt and other external factors, deviation will occur, leading to material overflow and damage to transportation equipment. Therefore, it is of great significance to detect the deviation status of the conveyor belt quickly and timely to ensure the safe and efficient operation of the transportation system. This paper presents an automatic detection method of belt deviation based on DeeplabV3+, which can detect the deviation of any position of belt conveyor. We have established a new belt edge dataset under real working conditions. In order to improve the deviation detection accuracy, we expand and erode the image after feature extraction, extract the centerline, and finally detect the deviation distance through the deviation detection module. Experiments show that this method can well balance the detection accuracy and detection speed. The processing speed of a single image is 0.32 s, and the conveyor belt edge detection error is less than 6mm, this method has good real-time performance and high precision, and can be applied to the production scene of underground coal mine.
This paper presents a method of haze removal and computer-generated holographic display of degraded images in coal mine. Firstly, the image enhancement of underground coal mine is realized by using the dark channel prior haze removal algorithm, which greatly weakens the shielding of coal dust and water mist in the roadway environment. Next, using the computer-generated hologram algorithm based on angular spectrum diffraction, the phase only hologram is generated with the haze removal image as the input. The peak signal-to-noise ratio (PSNR) of the reconstructed image of the red channel is 65.47dB, the PSNR of the reconstructed image of the green channel is 64.98dB, the PSNR of the reconstructed image of the blue channel is 65.78dB, and the average PSNR is 65.41dB. The simulation results show that high-quality reconstructed image can be obtained by combining dark channel prior and computer-generated hologram, and the image enhancement of underground coal mine is realized.
Pointer meters are widely used in transformer substations and other places because of their good robustness. Generally, the reading of pointer instrument cannot be read automatically, and can only rely on manual reading. With the development of robot and computer vision technology, it is possible to use inspection robot to obtain the reading of pointer instrument. The automatic reading of the analog meter should first detect the meter from the image, and then identify its reading. For the instability of traditional instrument detection, an analog meter automatic reading method based on YOLOv5s detector is proposed in this paper. Hough transform algorithm is used to detect the pointer and range, and then the meter reading is realized based on the angle method. Experiments show that the average processing time of a single image is 0.3s, and the reading error is less than 5%, which can meet the requirements of automatic reading in patrol inspection.
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