Edge YOLO: Real-Time Intelligent Object Detection System Based on Edge-Cloud Cooperation in Autonomous Vehicles
By Siyuan Liang, Hao Wu, Li Zhen, Qiaozhi Hua, Sahil Garg, Georges Kaddoum, Mohammad Mehedi Hassan, Keping Yu. IEEE transactions on intelligent transportation systems (Print), 2022
335 citations recorded by the source corpus.
Driven by the ever-increasing requirements of autonomous vehicles, such as traffic monitoring and driving assistant, deep learning-based object detection (DL-OD) has been increasingly attractive in intelligent transportation systems. However, it is difficult for the existing DL-OD schemes to realize the responsible, cost-saving, and energy-efficient autonomous vehicle systems due to low their inherent defects of low timeliness and high energy consumption. In this paper, we propose an object detection (OD) system based on edge-cloud cooperation and reconstructive convolutional neural networks, which is called Edge YOLO. This system can effectively avoid the excessive dependence on computing power and uneven distribution of cloud computing resources. Specifically, it is a lightweight OD framework realized by combining pruning feature extraction network and compression feature fusion network to enhance the efficiency of multi-scale prediction to the largest extent. In addition, we developed an autonomous driving platform equipped with NVIDIA Jetson for system-level verification. We experimentally demonstrate the reliability and efficiency of Edge YOLO on COCO2017 and KITTI data sets, respectively. According to COCO2017 standard datasets with a speed of 26.6 frames per second (FPS), the results show that the number of parameters in the entire network is only 25.67 MB, while the accuracy (mAP) is up to 47.3%.
Subfields: Computer Vision and Pattern Recognition, Electrical and Electronic Engineering, Neurology
View Paper: https://www.semanticscholar.org/paper/354d9af15bc8e3cd57b28f1f2e4dca4a63f54702
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Citations in the corpus
- A Comprehensive Review of YOLO Architectures in Computer Vision: From YOLOv1 to YOLOv8 and YOLO-NAS (2023, background): “YOLO’s real-time object detection capabilities have been invaluable in autonomous vehicle systems, enabling quick identification and tracking of various objects such as vehicles, pedestrians [11, 12], bicycles, and other obstacles [13, 14, 15, 16].”
- A Comprehensive Review of YOLO: From YOLOv1 to YOLOv8 and Beyond (2023, background): “YOLO’s real-time object detection capabilities have been invaluable in autonomous vehicle systems, enabling quick identification and tracking of various objects such as vehicles, pedestrians [1, 2], bicycles, and other obstacles [3, 4, 5, 6].”