Longitudinal Tear Detection of Conveyor Belt Based on Improved YOLOv7

Abstract

The complex environmental influences often complicate the detection of longitudinal tears in conveyor belts, resulting in insufficient detection accuracy, overlooked detection, and elevated false detection rates. In this study, we propose a new depth learning method specifically designed for detecting longitudinal tears in conveyor belts. This method employs a linear Charge-Coupled Device (CCD) camera to capture images of the conveyor belt. These images are subsequently processed with a modified version of You Only Look Once (YOLO)v7 model to identify instances of longitudinal tearing. The modified YOLOv7 model features Efficient Intersection over Union (EIoU) loss function as a substitute for the original loss function. Furthermore, a Simple Parameter-Free Attention Module (SimAM) is introduced in the detection head to improve detection accuracy. In this method, we introduced the SimSPPFCSPC module as a new spatial pyramid pooling model. This module enhances detection speed while maintaining detection accuracy. Experiment results demonstrate the effectiveness of the proposed method, achieving an impressive precision of 94.6% and a detection speed of approximately 110 Frames Per Second (FPS). Such accuracy and speed meet the requirements for online detection of longitudinal tearing in belt conveyors.