Research on Rigid Guide Deformation Detection Based on Line Structured Light 3-D Reconstruction Algorithm

Abstract

The rigid guide is an important part of the coal mine lifting system, serving a guiding function during the cage operation, making guide deformation detection crucial for ensuring safety in coal mine production. To address the issues of high detection safety costs, long maintenance times, and poor detection accuracy in the existing manual inspection mode, this article proposes installing detection devices such as industrial cameras and laser scanners on the hoisting container’s roof. During the operation of the lifting system, rigid guide contour laser stripe images are captured, and a laser and machine vision fusion reconstruction model is derived to complete the 3-D reconstruction of the guide point cloud model and obtain guide deformation parameter information. Building upon this, an adaptive center extraction and smoothing algorithm (ACES-R) for the reflective surface stripes of rigid guides is proposed. This method employs an adaptive dynamic contour tracking algorithm and incorporates laser stripe morphology features to effectively segment the laser stripe region. Under complex lighting conditions, including strong reflection interference, overlapping noisy laser segments, and uneven stripe widths, it accurately extracts guide contour information and reconstructs a complete guide point cloud model, outperforming traditional center extraction algorithms. In guide deformation measurement experiments, multiple repeated measurements show that the average error does not exceed 0.2 mm, and the standard deviation (SD) of the measurement results remains within 0.1 mm.