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Smart city article details
| Title | Traffic Sign Detection For Green Smart Public Transportation Vehicles Based On Light Neural Network Model |
|---|---|
| ID_Doc | 58661 |
| Authors | Ayachi R.; Afif M.; Said Y.; Abdelali A.B. |
| Year | 2022 |
| Published | Green Energy and Technology |
| DOI | http://dx.doi.org/10.1007/978-3-030-96429-0_4 |
| Abstract | Aiming to rise the security degree and the safety level of drivers, vehicles, and pedestrians, a traffic sign detection system is proposed in this work based on deep learning technologies. By developing the proposed assisting system, we contribute to build a new public smart transportation system used in smart cities and smart environments. Traffic sign detection presents one of the most important parts in an ADAS system due to its safety reasons. Detecting road sign can widely prevent people from accidents by respecting the traffic rules. Ensuring a reliable implementation on edge devices as field programmable gate arrays (FPGAs) presents an increasing challenge. To address this problem, we propose to build in this paper a new traffic sign detection system based on deep convolutional neural networks (DCNNs). The proposed detection system has been built based on YOLO as an objects detectors model in combination with SqueezeNet model which was used as lightweight backbone for features extraction. The use of SqueezeNet has been proposed to ensure a lightweight implementation on FPGA. In order to ensure the model implementation on FPGA, different optimizations techniques have been proposed. The proposed lightweight implementation of the traffic sign detection system has been performed on the pynq z1 platform. Training and testing experiments have been performed using the Chinese traffic sign detection (CTSD) dataset. Based on the experiments results, the proposed detection system achieved very interesting results in terms of detection accuracy and processing time. It achieves 96% mAP as a detection accuracy with 16 FPS as a processing time. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG. |
| Author Keywords | Advanced driver assistance system; Deep learning; Edge implementation; Pruning and quantization; Traffic sign detection |
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