Department of Telecommunication Engineering, University of Engineering and Technology, Peshawar 25000, Pakistan.
Department of Telecommunication Engineering, University of Engineering and Technology, Mardan 23200, Pakistan.
Sensors (Basel). 2021 Apr 26;21(9):3028. doi: 10.3390/s21093028.
Technologies and services towards smart-vehicles and Intelligent-Transportation-Systems (ITS), continues to revolutionize many aspects of human life. This paper presents a detailed survey of current techniques and advancements in Automatic-Number-Plate-Recognition (ANPR) systems, with a comprehensive performance comparison of various real-time tested and simulated algorithms, including those involving computer vision (CV). ANPR technology has the ability to detect and recognize vehicles by their number-plates using recognition techniques. Even with the best algorithms, a successful ANPR system deployment may require additional hardware to maximize its accuracy. The number plate condition, non-standardized formats, complex scenes, camera quality, camera mount position, tolerance to distortion, motion-blur, contrast problems, reflections, processing and memory limitations, environmental conditions, indoor/outdoor or day/night shots, software-tools or other hardware-based constraint may undermine its performance. This inconsistency, challenging environments and other complexities make ANPR an interesting field for researchers. The Internet-of-Things is beginning to shape future of many industries and is paving new ways for ITS. ANPR can be well utilized by integrating with RFID-systems, GPS, Android platforms and other similar technologies. Deep-Learning techniques are widely utilized in CV field for better detection rates. This research aims to advance the state-of-knowledge in ITS (ANPR) built on CV algorithms; by citing relevant prior work, analyzing and presenting a survey of extraction, segmentation and recognition techniques whilst providing guidelines on future trends in this area.
面向智能车辆和智能交通系统 (ITS) 的技术和服务继续彻底改变人类生活的许多方面。本文对当前自动车牌识别 (ANPR) 系统的技术和进展进行了详细调查，并对各种实时测试和模拟算法进行了全面的性能比较，包括涉及计算机视觉 (CV) 的算法。ANPR 技术能够通过识别技术检测和识别车辆的车牌。即使使用最好的算法，成功部署 ANPR 系统可能还需要额外的硬件来最大限度地提高其准确性。车牌状况、非标准化格式、复杂场景、相机质量、相机安装位置、对失真、运动模糊、对比度问题、反射、处理和内存限制、环境条件、室内/室外或白天/夜间拍摄、软件工具或其他基于硬件的限制的容忍度都可能会影响其性能。这种不一致性、挑战性的环境和其他复杂性使得 ANPR 成为研究人员感兴趣的领域。物联网开始塑造许多行业的未来，并为 ITS 开辟新途径。通过与 RFID 系统、GPS、Android 平台和其他类似技术集成，ANPR 可以得到很好的利用。深度学习技术在 CV 领域得到了广泛应用，以提高检测率。本研究旨在通过引用相关的先前工作，分析和展示提取、分割和识别技术，并提供该领域未来趋势的指南，从而推进基于 CV 算法的 ITS（ANPR）的知识现状。
