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.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.