An Object Detection System for Automatic Driving: MEC-YOLO Based on "Cloud-Edge-End"
Siyuan Liang, Xu Zhang · 2024
The massive machine type of communication (mMTC) of 5G is the basis for IoT (Internet of Things) development, which can promote automobile industry upgrades: intelligent and green. Automatic driving (AD) is an important trend in intelligent vehicles. With the development of computer vision (CV) technology, pure vision sensor-based solutions are dominating the AD of intelligent vehicles. Due to CV hardware platform energy and cost consumption constraints, meeting the detection speed and accuracy of vehicle AD requirements have become an obvious challenge, especially for new energy vehicles. Combining the advantages of edge computing and IoT based on mMTC, this paper proposes a system called mobile edge computing-you only look once (MEC-YOLO) to solve this problem, which is an energy-efficient and low-cost instance-level vision perception system for AD. In this system, the local hardware platform uses the improved object detection (OD) network to extract features from the locally collected visual data, processes them into a feature extraction queue, uploads them to the edge network nodes to complete the OD task, and transmits the prediction results and trains weights back to the local devices. In addition, the cloud computing center resource can be invoked to supplement edge nodes when the edge computing capacity is insufficient. Furthermore, we develop an AD platform equipped with Jetson for system validation, which improved the detection speed by 93.2% and reduced the GPU resource occupation rate by 90.8% compared to YOLOv4 on the KITTI dataset. The test results verified that the MEC-YOLO proposed in this paper has the advantages of fast detection speed, high accuracy, and low resource occupation rate compared with the traditional OD scheme.