Vision-based Automated Vehicle Recognition (VAVR) has attracted considerable attention recently. Particularly given the reliance on emerging deep learning methods, which have powerful feature extraction and pattern learning abilities, vehicle recognition has made significant progress. VAVR is an essential part of Intelligent Transportation Systems. The VAVR system can fast and accurately locate a target vehicle, which significantly helps improve regional security. A comprehensive VAVR system contains three components: Vehicle Detection (VD), Vehicle Make and Model Recognition (VMMR), and Vehicle Re-identification (VRe-ID). These components perform coarse-to-fine recognition tasks in three steps. In this article, we conduct a thorough review and comparison of the state-of-the-art deep learning--based models proposed for VAVR. We present a detailed introduction to different vehicle recognition datasets used for a comprehensive evaluation of the proposed models. We also critically discuss the major challenges and future research trends involved in each task. Finally, we summarize the characteristics of the methods for each task. Our comprehensive model analysis will help researchers that are interested in VD, VMMR, and VRe-ID and provide them with possible directions to solve current challenges and further improve the performance and robustness of models.

中文翻译：

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基于视觉的自动驾驶汽车识别

基于视觉的自动车辆识别（VAVR）最近引起了相当大的关注。特别是考虑到对具有强大特征提取和模式学习能力的新兴深度学习方法的依赖，车辆识别取得了重大进展。VAVR 是智能交通系统的重要组成部分。VAVR系统可以快速准确地定位目标车辆，极大地提高了区域安全性。一个综合的 VAVR 系统包含三个组件：车辆检测 (VD)、车辆制造和型号识别 (VMMR) 以及车辆重新识别 (VRe-ID)。这些组件分三个步骤执行从粗到细的识别任务。在本文中，我们对为 VAVR 提出的最先进的基于深度学习的模型进行了彻底的审查和比较。我们详细介绍了用于对所提出的模型进行综合评估的不同车辆识别数据集。我们还批判性地讨论了每项任务所涉及的主要挑战和未来的研究趋势。最后，我们总结了每个任务的方法的特点。我们全面的模型分析将帮助对 VD、VMMR 和 VRe-ID 感兴趣的研究人员，并为他们提供解决当前挑战的可能方向，并进一步提高模型的性能和鲁棒性。