Research on Recognition of Coal and Gangue Based on Laser Speckle Images

Abstract

Coal gangue image recognition is a critical technology for achieving automatic separation in coal processing, characterized by its rapid, environmentally friendly, and energy-saving nature. However, the response characteristics of coal and gangue vary greatly under different illuminance conditions, which poses challenges to the stability of feature extraction and recognition, especially when strict illuminance requirements are necessary. This leads to fluctuating coal gangue recognition accuracy in industrial environments. To address these issues and improve the accuracy and stability of image recognition under variable illuminance conditions, we propose a novel coal gangue recognition method based on laser speckle images. Firstly, we studied the inter-class separability and intra-class compactness of the collected laser speckle images of coal and gangue by extracting gray and texture features from the laser speckle images, and analyzed the performance of laser speckle images in representing the differences between coal and gangue minerals. Subsequently, coal gangue recognition was achieved using an SVM classifier based on the extracted features from the laser speckle images. The fusion feature approach achieved a recognition accuracy of 94.4%, providing further evidence of the feasibility of this method. Lastly, we conducted a comparative experiment between natural images and laser speckle images for coal gangue recognition using the same features. The average accuracy of coal gangue laser speckle image recognition under various lighting conditions is 96.7%, with a standard deviation of the recognition accuracy of 1.7%. This significantly surpasses the recognition accuracy obtained from natural coal and gangue images. The results showed that the proposed laser speckle image features can facilitate more stable coal gangue recognition with illumination factors, providing a new, reliable method for achieving accurate classification of coal and gangue in the industrial environment of mines.

Precise detection of coal and gangue based on natural γ-ray

“…Reasonable time for closing the caving-opening depends on the mixing degree of coal gangue 10 – 12 . Therefore, accurate, real-time and effective technology for identifying coal gangue mixing degree is an effective measure to realize automation and intelligent mining in coal mines, as well as promoting the intelligent industrialization of coal industry 13 – 15 . It can effectively reduce labor cost, enhance the safety of coal mining operations, reduce equipment maintenance expenses, and significantly improve the coal mining extraction rate, ultimately leading to higher productivity and efficiency 7 , 16 , 17 .…”