Recognition of unwanted objects in coal flow based on gray pyramid convolution model of machine vision

The belt conveying of coal flow is an important link in the process of coal energy production, and the recognition of foreign matters in coal flow is an indispensable task in the construction of intelligent mining. In view of the problem of the unwanted object recognition of the coal flow in belt conveyor monitoring system, a dual pyramid convolution recognition model was constructed by using the machine vision grayscale. Firstly, a method of machine vision image graying based on weight training was proposed. According to the color difference and motion difference between foreign matters and coal transported by conveyor belt, Lucas-Kanade optical flow algorithm was used to realize the recognition of abnormal colors and abnormal motion pixels. At the same time, combined with illumination compensation processing, the real-time image of coal flow is preprocessed by brightness correction and irrelevant pixel removal. Secondly, a pyramid network was constructed to extract the multi-scale fusion characteristics of moving foreign matters in coal flow. This paper constructed a two-channel network model of conventional feature extraction pathway and feature difference pathway to realize the recognition of coal and foreign matter features. Finally, the foreign matters in the monitoring images of coal flow conveyor belt in Caojiatan coal mine of Shaanxi coalfield were identified. The results show that the foreign body recognition accuracy of coal conveyor belt based on machine vision grayscale two-channel pyramid convolution model is 95.7%, which is 2.1% higher than that of classical convolution network model. Compared with traditional image gray-scale fixed weight recognition model, the accuracy of foreign object recognition model based on adaptive weight training is improved by 5.2%.