Hazard source detection of longitudinal tearing of conveyor belt based on deep learning

Abstract

Belt tearing is the main safety accident of belt conveyor. The main cause of tearing is the doped bolt and steel in the conveying belt. In this paper, the bolt and steel are identified as the Hazard source of tear. In this paper, bolt and steel are defined as the risk sources of tearing. Effective detection of the source of danger can effectively prevent the occurrence of conveyor belt tearing accidents. Here we use deep learning to detect the hazard source image. We improved on the SSD(Single Shot MultiBox Detector) model. Replace the original backbone network with an improved Shufflenet_V2, and replace the original position loss function with the CIoU loss function. Additionally, it compares this new approach to previous methods. The proposed model has surpassed other state-of-art methods with more than 94% accuracy. In addition, when deployed without GPU acceleration, the detection speed can reach 20fps. It can meet the requirements of real-time detection. The experimental results show that the proposed model can realize the online detection of hazard sources, so as to prevent longitudinal tearing of conveyor belt.

HLG-YOLOv7: small object detection in conveyor belt damage based on leveraging hybrid local and global features

“…Belt conveyors, due to their stable and efficient long-distance conveying capability, are regarded as one of the primary equipment for bulk material transportation. Among them, the conveyor belt, as an important component, plays a critical role in bearing and conveying materials [1]. However, due to long-term operation in harsh environments, conveyor belts are susceptible to factors such as sharp materials and misalignment, resulting in surface wear, scratches, and longitudinal tearing.…”