A detection method for large blocks in underground coal transportation

Abstract

In view of problems that the existing detection methods for large blocks in underground coal transportation cannot detect quantity of large blocks and detection accuracy is not high, a detection method for large blocks in underground coal transportation based on improved HED neural network and Canny operator was proposed. Firstly, extracted reflection component combined with edge reservation filtering method is used to preprocess collected image, so as to enhance the image brightness and contrast and deepen image edge information. Then, the preprocessed image is substituted into the fusion model of improved HED neural network and Canny operator to obtain the continuous large blocks edge image, and the binarization filled image is obtained by doing non-operation according to the edge image. The large blocks in the binarization image are marked with rectangle and the pixel number and area of the large blocks are calculated. Finally, the number of large blocks is counted and judge whether the area of large blocks is higher than the set threshold. If the area is higher than the set threshold, the alarm will be given.The experimental results show that the detection method for large blocks in underground coal transportation based on improved HED neural network with Canny operator has a good edge detection effect, which can effectively reduce the image edge detection error, and count the number of large blocks, calculate the area of large blocks.

Keywords

• belt transportation
• coal transportation
• large blocks detection
• reflection component
• hed neural network
• edge detectio