Coal/gangue recognition accuracy based on infrared image with liquid intervention under different mixing degree

Automatic identification of coal gangue with high accuracy is vital to intelligent top coal caving mining. Aiming at the types of coal gangue that are difficult to recognize with small grayscale differences, a new method of coal/gangue recognition based on “liquid intervention+infrared detection” was proposed. The infrared images of coal gangue mixed samples with different mixing degree and different time under the condition of liquid intervention were collected. The mixing degree of coal gangue image is quantitatively calculated based on the ImageJ software, and the influence of coal gangue mixing degree, intervention time and image processing method on the recognition accuracy were analyzed. The technical path to improve the coal/gangue recognition accuracy with liquid intervention is discussed from the perspective of infrared temperature change rate field. The results show that for the types of coal gangue with small gray difference in visible image, the method of “liquid intervention+infrared detection” can be used to improve the accuracy of coal/gangue recognition. Under different coal gangue mixing degree, there is a significant temperature drop in the area where the coal sample is located in the infrared image after liquid intervention, which can be used as the basis for automatic recognition of coal gangue mixing degree. When the gangue mixing rate is low, the coal sample temperature plays a controlling role in the change of infrared image characteristics of coal gangue mixture; When the mixed gangue rate is less than 20%,the recognition accuracy of increases with the increasing of gangue mixed ratio, and the average accuracy within 10 s after liquid intervention is about 85.78%; When the gangue mixing rate is in the range of 20%-60%,the recognition accuracy is high and stable, and the average accuracy is about 94.38%,and the liquid intervention time has little influence on it. The high accuracy area presents the distribution characteristics of “inclined strip”. When the gangue mixing rate is larger than 60%,the average recognition accuracy of different processing methods shows a downward trend, and the discreteness increases sharply; The reduction of average infrared temperature difference of infrared image is the root reason of recognition accuracy decreasing in the later stage of mixed coal gangue drawing. The change degree of infrared temperature field can be increased by selecting reasonable liquid intervention parameters such as liquid type, temperature, intervention volume, so as to improve the recognition accuracy.