Recent advancements in deep learning have provided powerful tools for intelligent vehicle tasks, particularly in the field of perception. However, achieving real-time performance with low power consumption remains a challenge included in the hot research topic known as green deep learning. In this paper, we present a comparative analysis of various YOLOv5 weights trained on the KITTI and SHIFT datasets using two platforms with different power consumption profiles: the NVIDIA Jetson AGX Xavier and a desktop computer equipped with a NVIDIA GTX 1080 Ti. Our analysis focuses on the average inference time and precision metrics for road objects detection, a key task for intelligent vehicles. Additionally, we apply TensorRT to optimize and accelerate the architecture on both platforms, resulting in significant speed improvements, particularly on the low-power Jetson AGX Xavier (30W). Our ultimate goal is to implement our whole autonomous driving architecture on several Jetson AGX Xaviers connected to a PC where the hyper-realistic CARLA simulator, is replicating the real-world autonomous vehicle environment. We obtain compelling validation results on KITTI and CARLA, achieving real-time performance on a lightweight Jetson AGX Xavier with a powerful object detector such as YOLOv5m.
Green Deep Learning: Comparative Study of Road Object Detectors Between Jetson Boards and PC
Lect. Notes in Networks, Syst.
Iberian Robotics conference ; 2023 ; Coimbra, Portugal November 22, 2023 - November 24, 2023
27.04.2024
12 pages
Aufsatz/Kapitel (Buch)
Elektronische Ressource
Englisch
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