Improved YOLOv7-based algorithm for elevator passenger detection

Abstract

The task of identifying the number of passengers in an elevator plays an important role in optimizing the elevator algorithm and ensuring the safety of elevator passengers. In order to enable the elevator passenger-carrying algorithm to be deployed in edge devices, a lightweight elevator passenger-carrying algorithm based on improved yolov7 is designed, which reduces the model size while ensuring high precision. The proposed algorithm adopts a more lightweight attention mechanism structure in the head of the network. And made the lift-person-detection dataset as the experimental dataset. The dataset has nearly 2,000 image samples, including 900 training set images and 600 test set images. Experiments show that the proposed model achieves 98.9% mAP recognition accuracy. Compared with the 71.21MB model size of the original yolov7 network, the model size of the model proposed in this paper is reduced to 63.24MB, and the volume is reduced by 11.13%.