A lightweight bus passenger detection model based on YOLOv5

The bus passenger detection algorithm is a key component of a public transportation bus management system. The detection techniques based on the convolutional neural network have been widely used in bus passenger detection. However, they require high memory and computational requirements, which hinder the deployment of bus passenger detectors in the bus system. In this paper, a lightweight bus passenger detection model based on YOLOv5 is introduced. To make the model more lightweight, the inner and outer cross-stage bottleneck modules, called ICB and OCB, respectively, are proposed. The proposed module reduces the quantity of parameter and floating point operations and increases the detection speed. In addition, the neighbour feature attention pooling is adopted to improve detection accuracy. The performance of the lightweight model on the bus passenger dataset is empirically demonstrated. The experiment results demonstrate that the proposed model is lightweight and efficient. Compared lightweight YOLOv5n with the original algorithm, the model weight is reduced by 31% to 2.6M, and the detection speed is increased by 6% to 40FPS without an accuracy drop. We propose a lightweight bus passenger detector based on the network YOLOv5 to improve the bus passenger detection speed. The inner cross bottleneck ICB and outer cross bottleneck OCB are introduced into the YOLOv5 network to achieve model compression, maintain detection accuracy and improve speed. The NFP pooling module is incorporated into the network to aggregate discriminative features and improve the detection accuracy of the model.image