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- W4309636800 abstract "With road congestion and soaring gas prices, electric bikes and bicycles have become the new favorites for short distance travel. But with this comes more safety hazards, more and more people will drive their electric bikes and bicycles into elevators and bring them to floors, causing safety hazards in elevators and hallways. In this paper, we propose a deep learning-based two-wheeled vehicle entry ban system, so that when the elevator detects two types of banned targets, such as electric vehicles and bicycles, entering the elevator, an alarm will be issued in time to remind passengers to drive the banned targets out of the elevator, and at the same time, the elevator will be stopped by pressing the elevator emergency stop button through an electrical signal control peripheral switch, and in the case that the banned targets are not driven out of the elevator in time, the system will notify the relevant staff through the cloud server. For the optimization of the YOLO V4 algorithm, Backbone adopts a lightweight network, which greatly reduces the model training time and parameters. By changing the attention mechanism and Neck structure, the detection accuracy of the network is slightly improved, making it more suitable for the elevator prohibition system." @default.
- W4309636800 created "2022-11-28" @default.
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- W4309636800 date "2022-11-18" @default.
- W4309636800 modified "2023-09-26" @default.
- W4309636800 title "Research on Two-Wheeled Vehicle Entry Ban System Based on Deep Learning" @default.
- W4309636800 cites W2090819008 @default.
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- W4309636800 doi "https://doi.org/10.1007/978-3-031-20601-6_21" @default.
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