Research on the Strong Generalization of Coal Gangue Recognition Technology Based on the Image and Convolutional Neural Network under Complex Conditions

Abstract

A coal gangue image recognition method based on complex conditions is proposed to address the current issue of image-based coal gangue recognition being greatly affected by complex conditions. First, complex conditions such as different shooting backgrounds, shooting distances, and lighting intensities are set to simulate the underground coal mining environment. Then, based on three convolutional neural network algorithms, the coal gangue recognition model is established, and the influence of different complex conditions on coal gangue image recognition is analyzed. At the same time, a network model with a strong generalization ability is determined. The results show that the accuracy of coal gangue image recognition has no obvious regularity under different shooting background conditions, and complex environments should be the primary factor affecting the accuracy of coal gangue image recognition. The accuracy of coal gangue image recognition is negatively correlated with the increase in shooting distance, and strong light conditions are conducive to improving the accuracy of coal gangue image recognition. The LeNet network model has the strongest generalization ability, which can meet the requirements of recognition accuracy and respond quickly. The accuracy of coal gangue image recognition under different complex conditions can reach more than 0.99, and the average single image recognition time is only 177 ms. This article studies the influence law of different complex conditions on the recognition of coal and gangue images and confirms that the LeNet network has strong generalization ability, achieving accurate and fast recognition of coal gangue images under complex conditions and providing a reference basis for the deployment of underground coal gangue sorting.