A new machine vision detection method for identifying and screening out various large foreign objects on coal belt conveyor lines

Abstract

Large foreign object transporting by coal mine conveyor belt may lead to production safety hazards. To reduce safety accidents during coal mining, a large foreign object detection method based on machine vision is proposed in this paper. An adaptive weighted multi-scale Retinex (MSR) image enhancement algorithm is proposed to improve the captured image quality of the belt conveyor line. An improved multi-scale template matching algorithm is designed by combining the frame difference and area methods to screen and identify large foreign objects mixed in coals. The multi-layer perceptron (MLP) network optimized by the Gray Wolf algorithm is introduced to identify the large objects. Experimental results show that the identification accuracy reaches 98.8% for the large foreign objects. Furthermore, industrial field test is carried out in the Gaoyang coal mine, and the filed test results demonstrate that the identification accuracy of the proposed method is more than 95%. Hence, the proposed method meets the industrial detection requirements and can be used in practices for detecting the large foreign objects.