An Improved Lightweight YOLOv5s-Based Method for Detecting Electric Bicycles in Elevators

Abstract

The increase in fire accidents caused by indoor charging of electric bicycles has raised concerns among people. Monitoring EBs in elevators is challenging, and the current object detection method is a variant of YOLOv5, which faces problems with calculating the load and detection rate. To address this issue, this paper presents an improved lightweight method based on YOLOv5s to detect EBs in elevators. This method introduces the MobileNetV2 module to achieve the lightweight performance of the model. By introducing the CBAM attention mechanism and the Bidirectional Feature Pyramid Network (BiFPN) into the YOLOv5s neck network, the detection precision is improved. In order to better verify that the model can be deployed at the edge of an elevator, this article deploys it using the Raspberry Pi 4B embedded development board and connects it to a buzzer for application verification. The experimental results demonstrate that the model parameters of EBs are reduced by 58.4%, the computational complexity is reduced by 50.6%, the detection precision reaches 95.9%, and real-time detection of electric vehicles in elevators is achieved.