Utilizing the latest advances in Artificial Intelligence (AI), the computer vision community is now witnessing an unprecedented evolution in all kinds of perception tasks, particularly in object detection. Based on multiple spatially separated perception nodes, Cooperative Perception (CP) has emerged to significantly advance the perception of automated driving. However, current cooperative object detection methods mainly focus on ego-vehicle efficiency without considering the practical issues of system-wide costs. In this paper, we introduce VINet, a unified deep learning-based CP network for scalable, lightweight, and heterogeneous cooperative 3D object detection. VINet is the first CP method designed from the standpoint of large-scale system-level implementation and can be divided into three main phases: 1) Global Pre-Processing and Lightweight Feature Extraction which prepare the data into global style and extract features for cooperation in a lightweight manner; 2) Two-Stream Fusion which fuses the features from scalable and heterogeneous perception nodes; and 3) Central Feature Backbone and 3D Detection Head which further process the fused features and generate cooperative detection results. A cooperative perception platform is designed and developed for CP dataset acquisition and several baselines are compared during the experiments. The experimental analysis shows that VINet can achieve remarkable improvements for pedestrians and cars with 2x less system-wide computational costs and 12x less system-wide communicational costs.
翻译:利用人工智能(AI)的最新进展,计算机视觉界目前正在目睹各种认知任务,特别是目标探测方面前所未有地演变。基于多个空间分离的认知节点,合作感知(CP)已经出现,大大提高了自动驾驶的观念。然而,当前合作性物体探测方法主要侧重于自我车效率,而没有考虑全系统成本的实际问题。在本文件中,我们引入了VINet,即一个统一的深层次学习基点网络,用于可缩放、轻量和多种合作3D对象探测。VINet是从大规模系统一级实施的角度设计的第一个CP方法,可分为三个主要阶段:(1) 全球预处理前和轻度地段提取,以轻度方式将数据制成全球风格并提取合作特征;(2) 将可缩放和混杂感知节点的特征结合起来的双层裂开;(3) 中央特性后骨和3D探测头,进一步处理若干整合特征并产生合作性检测结果。一个合作性概念化和轻度通信平台可分为三个主要阶段:1) 合作性概念平台,将数据制成为全球模式,在全范围内的实验性成本和12级计算中可降低。