SHINE：基于深度学习的无障碍停车管理系统
SHINE: Deep Learning-Based Accessible Parking Management System
AI总结：
针对韩国无障碍停车位滥用问题，本文提出基于深度学习的Shine系统，通过目标检测算法识别车辆、车牌与残疾人徽章并与中央服务器协同验证权限，模型平均精度均值达92.16%，有效提升了城市停车管理效率。
摘要：
科技进步推动城市不断扩张，导致包括韩国在内的全球私家车数量大幅增加。然而，车辆数量的逐渐增加不可避免地引发了停车相关问题，包括滥用为残疾人指定的残疾人停车位（以下简称无障碍停车位）。传统车牌识别（LPR）系统在实时解决此类问题上已被证明效率低下，原因在于监控摄像头的高帧率、自然和人工噪声的存在，以及阻碍这些系统进行检测和识别的光照和天气条件变化。随着停车 4.0 概念的兴起，许多传感器、物联网和基于深度学习的方法已被应用于自动 LPR 和停车管理系统。尽管如此，研究表明，韩国在管理无障碍停车位方面仍需要一个稳健且高效的模型。为了解决这个问题，我们提出了一种名为 Shine 的新型系统，该系统使用基于深度学习的目标检测算法来检测车辆、车牌和残疾人徽章（以下简称卡片、徽章或通行徽章），并通过与中央服务器协调来验证驾驶员使用无障碍停车位的权利。我们的模型实现了 92.16% 的平均精度均值，有望解决无障碍停车位滥用问题，并为城市环境中高效且有效的停车管理做出重大贡献。
英文摘要：
The ongoing expansion of urban areas facilitated by advancements in science and technology has resulted in a considerable increase in the number of privately owned vehicles worldwide, including in South Korea. However, this gradual increment in the number of vehicles has inevitably led to parking-related issues, including the abuse of disabled parking spaces (hereafter referred to as accessible parking spaces) designated for individuals with disabilities. Traditional license plate recognition (LPR) systems have proven inefficient in addressing such a problem in real-time due to the high frame rate of surveillance cameras, the presence of natural and artificial noise, and variations in lighting and weather conditions that impede detection and recognition by these systems. With the growing concept of parking 4.0, many sensors, IoT and deep learning-based approaches have been applied to automatic LPR and parking management systems. Nonetheless, the studies show a need for a robust and efficient model for managing accessible parking spaces in South Korea. To address this, we have proposed a novel system called, Shine, which uses the deep learning-based object detection algorithm for detecting the vehicle, license plate, and disability badges (referred to as cards, badges, or access badges hereafter) and verifies the rights of the driver to use accessible parking spaces by coordinating with the central server. Our model, which achieves a mean average precision of 92.16%, is expected to address the issue of accessible parking space abuse and contributes significantly towards efficient and effective parking management in urban environments.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
