Performance Evaluation of Recent Object Detection Models for Traffic Safety Applications on Edge

Anilcan Bulut, Fatmanur Ozdemir, Y. S. Bostanci, M. Soyturk
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Abstract

Real-time objection detection is becoming more important and critical in all application areas, including Smart Transport and Smart City. From safety/security to resource efficiency, real-time image processing approaches are used more than ever. On the other hand, low-latency requirements and available resources present challenges. Edge computing integrated with cloud computing minimizes communication delays but requires efficient use of resources due to its limited resources. For example, although deep learning-based object detection methods give very accurate and reliable results, they require high computational power. This overhead reveals a need to implement deep learning models with less complex architectures for edge deployment. In this paper, the performance of evolving deep learning models with their lightweight versions such as YOLOv5-Nano, YOLOX-Nano, YOLOX-Tiny, YOLOv6-Nano, YOLOv6-Tiny, and YOLOv7-Tiny are evaluated on a commercially available edge device. The results show that YOLOv5-Nano and YOLOv6-Nano with their TensorRT versions can provide real-time applicability in approximately 35 milliseconds of inference time. It is also observed that YOLOv6-Tiny gives the highest average precision while YOLOv5-Nano gives the lowest energy consumption when compared to other models.
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基于边缘的交通安全目标检测模型性能评价
实时目标检测在包括智慧交通和智慧城市在内的所有应用领域变得越来越重要和关键。从安全/保障到资源效率,实时图像处理方法的使用比以往任何时候都多。另一方面,低延迟需求和可用资源带来了挑战。与云计算集成的边缘计算可以最大限度地减少通信延迟,但由于资源有限,需要有效利用资源。例如,尽管基于深度学习的对象检测方法给出了非常准确和可靠的结果,但它们需要很高的计算能力。这种开销表明,需要为边缘部署使用不太复杂的架构来实现深度学习模型。在本文中,不断发展的深度学习模型及其轻量级版本(如YOLOv5-Nano、YOLOX-Nano、YOLOX-Tiny、YOLOv6-Nano、YOLOv6-Tiny和YOLOv7-Tiny)的性能在商用边缘设备上进行了评估。结果表明,YOLOv5-Nano和YOLOv6-Nano及其TensorRT版本可以在大约35毫秒的推理时间内提供实时适用性。与其他型号相比,YOLOv6-Tiny的平均精度最高,而YOLOv5-Nano的能耗最低。
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