A coal-gangue optimization identification method

Aiming at problem that target detection of coal-gangue image is not accurate due to wear of conveyor belt, which affects identification accuracy of coal-gangue, a coal-gangue optimization identification method is proposed. After pre-processing of collected images such as cutting, denoising and grayscale, the trained cornernet-squeeze deep learning model is used to judge whether there is coal or gangue to be detected in the images. If there is, position of coal or gangue in the images is located, which can effectively reduce background interference of conveyor belt during detection. The location area is analyzed by gray histogram, then according to third moment characteristic parameter of image gray histogram, coal-gangue is classified to determine whether it is coal or gangue to improve identification accuracy. The experimental results show that the method has high identification accuracy and good real-time performance with identification accuracy of 91.3% and identification time of 41 ms for single image.