A new solar tracking sensor based on image recognition is proposed and designed to solve the problem of low accuracy of photoelectric tracking in photovoltaic power generation. The sensor can directly output its angular deviation from the sun, and its mechanical structure and working principle are analysed in detail. We use a high-precision camera to collect the image of the two slots on the projector surface and use the Hough transform to identify the image of the light seam. After obtaining the linear equation for the two slots, the coordinate of the intersection point is found, and the calculation of the solar altitude and azimuth can be realized. We have improved the Hough transform scheme by using the skeleton image of the slots instead of the edge image. The improvement of the scheme has been proved to effectively improve the detection accuracy. A calibration test board is used to test the sensor and experimental results show that the scheme can achieve the measurement of azimuth and altitude with the accuracy of be 0.05°, which can meet the detection accuracy requirements of the solar tracking in photovoltaic power generation and many other photoelectric tracking implementations.

Considering the low efficiency during the process of traditional calibration for digital-display vibrometers, an automatic calibration system for vibrometers based on machine vision is developed. First, an automatic vibration control system is established on the basis of a personal computer, and the output of a vibration exciter on which a digital-display vibrometer to be calibrated is installed, is automatically adjusted to vibrate at a preset vibration level and a preset frequency. Then the display of the vibrometer is captured by a digital camera and identified by means of image recognition. According to the vibration level of the exciter measured by a laser interferometer and the recognized display of the vibrometer, the properties of the vibrometer are calculated and output by the computer. Image recognition algorithms for the display of the vibrometer with a high recognition rate are presented, and the recognition for vibrating digits and alternating digits is especially analyzed in detail. Experimental results on the built-up system show that the prposed image recognition methods are very effective and the system could liberate operators from boring and intense calibration work for digital-display vibrometers

Sorting coal and gangue is important in raw coal production; accurately identifying coal and gangue is a prerequisite for effectively separating coal and gangue. The method of extracting coal and gangue using image grayscale information can effectively identify coal and gangue, but the recognition rate of the sorting process based on image grayscale information needs to substantially higher than that which is needed to meet production requirements. A sorting method of coal and gangue using object surface grayscale-gloss characteristics is proposed to improve the recognition rate of coal and gangue. Using different comparative experiments, bituminous coal from the Huainan area was used as the experimental object. It was found that the number of pixel points corresponding to the highest level grey value of the grayscale moment and illumination component of the coal and gangue images were combined into a total discriminant value and used as input for the best classification of coal and gangue using the GWO-SVM classification model. The recognition rate could reach up to 98.14%. This method sorts coal and gangue by combining surface greyness and glossiness features, optimizes the traditional greyness-based recognition method, improves the recognition rate, makes the model generalizable, enriches the research on coal and gangue recognition, and has theoretical and practical significance in enterprise production operations.