Design and Implementation of W-shaped Steel Band Hole Image Recognition System Based on OpenCV-Python

Abstract

The mine Roofbolter is widely used in drilling and supporting of underground roof and side wall, which greatly alleviates the imbalance of driving anchor. However, with the progress of sensor technology, electrical and electronic technology, electro-hydraulic proportional control technology and the continuous improvement of users’ requirements for the safety and efficiency of bolt support, domestic and foreign mining equipment manufacturers have carried out the development of automatic bolt and anchor cable support equipment, especially to improve the performance requirements of control system and develop intelligent visualization technology. At present, the hole location in the process of drilling still needs to be identified and located by human eyes, which is inefficient and the working environment is a great threat to the physical and mental health of personnel, so it is urgent to introduce computer technology and artificial intelligence technology. In this paper, with the help of image recognition technology, machine vision recognition replaces human eye recognition of hole location, and puts forward an image recognition technology of W-shaped steel belt hole recognition system based on OpenCV to assist the scheme, so as to ensure the safety and efficiency of roadway support. This system uses pychar, opencv and python language to realize the recognition and positioning of W-shaped steel strip hole. The scheme has the advantages of small amount of calculation, high accuracy and fast recognition speed. This research conclusion provides a reference for the further spatial positioning analysis of W-shaped steel belt hole, and improves the efficiency of underground support operation and personnel safety.