AI摄像头:在设备上实时识别车牌号码

Taewan Kim, Chunghun Kang, Yongsung Kim, Seungji Yang
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引用次数: 1

摘要

近年来,具有基于人工智能(AI)视频分析功能的智能监控摄像机已经普及。它们有望将高保真度、情境丰富的传感技术带入我们的家乡和工作场所,使我们的生活更智能、更安全。尽管取得了显著且无可争议的进步,但人工智能相机仍然局限于适当的卷积神经网络(cnn)模型,更重要的是,它不容易在边缘设备和(云)服务器之间设计一个健壮的系统架构,用于现实世界的应用。为了解决这些限制,我们开发了一种商业化的人工智能摄像头,可以安装在我们用来识别车牌的任何位置,结合了前端和后端智能。在智能前端系统中,我们设计了三种独特的基于AI图像的cnn模型,用于车牌角点检测和字符、数字的顺序识别。为了不断提高相机上AI功能的准确性,它与后端智能服务器相连,AI相机上的当前模型随着新传入的数据不断更新,不断适应。我们进行了一系列实验,显示了新架构的高精度和多功能性,同时产生了可以实际实施的稳健结果。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
AI Camera: Real-time License Plate Number Recognition on Device
Intelligent surveillance cameras with artificial intelligence (AI)-based video analytic function have become pervasive in recent years. They hold the promise of bringing high fidelity, contextually rich sensing into our home town and workplaces as a means of making our life smarter and safer. Despite remarkable and indisputable advances, AI cameras are still limited in the proper convolutional neural networks (CNNs) model, and more importantly, do not easily design a robust system architecture between edge device and (cloud) server for real-world applications. Towards addressing these limitations, we have developed an commercialized AI camera can be installed at any position that we use to recognize the license plate incorporating front-end and back-end intelligence. For intelligent front-end system, we designed three unique CNNs models on AI cmear for detecting license plate with its corner points and recognizing the characters and numbers sequentially. To increase the accuracy of AI functions on camera continuously, it is connect to back-end intelligence server where the current models on AI camera is updating with new incoming data in a continual process of adaptation. We conducted a series of experiments, showing high accuracy and versatility of the new architecture, while yielding robust results that can be practically implemented.
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