论文标题
边缘YOLO：基于自动驾驶汽车的边缘云合作的实时智能对象检测系统
Edge YOLO: Real-Time Intelligent Object Detection System Based on Edge-Cloud Cooperation in Autonomous Vehicles
论文作者
论文摘要
在自动驾驶汽车（例如交通监控和驾驶助手）的不断增长的要求下，基于深度学习的对象检测（DL-OD）在智能运输系统中越来越有吸引力。但是，由于其固有的及时性和高能量消耗的固有缺陷，现有的DL-OD计划很难实现负责任，节能和节能的自动驾驶汽车系统。在本文中，我们提出了一个基于边缘云合作和重建卷积神经网络的对象检测系统，该系统称为边缘Yolo。该系统可以有效地避免过度依赖计算能力和云计算资源分布不均匀的分布。具体而言，这是通过结合修剪特征提取网络和压缩特征融合网络实现的轻量级OD框架，以最大程度地提高多尺度预测的效率。此外，我们开发了一个配备了NVIDIA JETSON的自主驾驶平台进行系统级验证。我们在实验上分别在COCO2017和KITTI数据集上展示了边缘Yolo的可靠性和效率。根据COCO2017的标准数据集，速度为每秒26.6帧（FPS），结果表明，整个网络中的参数数量仅为25.67 MB，而精度（地图）最高为47.3％。
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%.
