Research on Optimization of Belt Conveyor Foreign Object Detection System Based on Improved YOLOv5 Algorithm and Deep Learning Enhancement

Abstract

This study aims to design a belt conveyor foreign object recognition system based on the Easy Language programming environment and the YOLOv5 deep learning model, with the goal of enhancing safety and efficiency on the production line. The overall architecture of the system encompasses core modules such as data collection, image preprocessing, model training, and result output. The system employs Easy Language as its programming language and integrates the YOLOv5 object detection algorithm. Through real-time video monitoring, it precisely identifies foreign objects and belt damage during the transportation process of the belt conveyor, effectively preventing and reducing accidents and losses on the production line. Experiments have demonstrated that the system significantly enhances the accuracy and efficiency of foreign object recognition in continuous conveyor belt operations.