测试技术学报2024，Vol.38Issue(2)：100-108,9.DOI:10.3969/j.issn.1671-7449.2024.02.002
基于太赫兹成像的输送带撕裂检测与分类识别研究
Research on Conveyor Belt Tear Detection and Classification Based on Terahertz Imaging
蒋悦 1凌平平 2徐伟2
作者信息
- 1. 天津工业大学 电子与信息工程学院,天津 300387||天津市光电检测技术与系统重点实验室,天津 300387
- 2. 天津工业大学 电子与信息工程学院,天津 300387
- 折叠
摘要
Abstract
Aiming at the problems of existing conveyor belt tear detection methods such as low sensitivity and safety,and inability to eliminate the impact of complex working environments,a conveyor belt tear detection method based on terahertz imaging technology was proposed.This method first designs and builds a continuous wave terahertz reflective imaging system to collect terahertz images of conveyor belt tears;And then performing processing such as filtering on the original image to obtain a low noise terahertz image;Finally,an automatic classification and recognition system for terahertz images based on machine learning is established.The system extracts the statistical and geometric features of the grayscale histogram of terahertz images,con-structs a terahertz image feature library,and uses feature selection to remove feature redundancy.Finally,combined with a classifier,automatic classification and recognition of conveyor belt tear types is achieved.The results show that the feasibility of terahertz wave imaging technology for detecting conveyor belt tears has been verified through imaging experiments on conveyor belts using a terahertz reflective imaging system;The terahertz image automatic classification and recognition system achieves automatic classification and recognition of three types of tear on conveyor belts.Under combined features,the classification accuracy using Support Vector Machine(SVM)can reach 91.6%.This study lays the foundation for the application of terahertz imaging technology in conveyor belt tear detection.关键词
成像系统/太赫兹成像/输送带/撕裂检测/特征提取/机器学习Key words
imaging systems/Terahertz imaging/conveyor belt/tear detection/feature extraction/machine learning分类
信息技术与安全科学引用本文复制引用
蒋悦,凌平平,徐伟..基于太赫兹成像的输送带撕裂检测与分类识别研究[J].测试技术学报,2024,38(2):100-108,9.
