以深度學習架構實現之適用於複雜道路環境之車牌辨識系統

A Novel License Plate Recognition System for Complex Road Environment with Deep Learning

謝亦信(Sie Yi-Sin)

指導教授 ： 林政宏

國立臺灣師範大學/科技與工程學院/電機工程學系/碩士(2019年)

https://doi.org/10.6345/NTNU201900464

摘要

因應智慧城市的發展，車牌辨識系統的發展對竊盜車輛調查、犯罪車輛追蹤和車流監控等車輛管理的需求迫切重要。例如，為了追蹤被盜或犯罪車輛，必須調閱特定範圍內所有路口監視器畫面，以人力檢視監視畫面以找尋特定車輛與車牌，相當耗時費工，而透過應用於路口監視器的車牌辨識系統，可以快速自動搜尋車輛車牌，建構車輛行進路線，大量減少人力負擔，對犯罪偵防將有重大助益。

近年來車牌辨識技術已經非常成熟的應用於智慧停車場、交通收費系統等場域，然而運用於路口監視器影像，會面臨諸多挑戰，包括車牌在畫面比例過小、光源不穩定、拍攝角度和車輛移動造成車牌字元模糊、複雜的道路環境、廣告車牌、交通號誌、路名指標等問題。

傳統車牌辨識系統方法分為三個步驟，包括偵測車牌、車牌字元切割以及字元辨識。本論文提出一個基於深度學習架構的階層式車牌辨識系統，首先在畫面中偵測車牌並將車牌影像擷取下來，接著針對車牌影像執行字元辨識，透過兩階段的方法，增加字元在畫面的比例，進而提高字元辨識準確率，實驗結果顯示，車牌偵測率為98.14%，字元辨識率為97.37%，系統執行速度為23.81 fps。另外，我們使用AOLP資料集測試，測試結果顯示，我們提出的方法相較於其他方法，在車牌偵測以及字元辨識皆有較高的車牌偵測率以及字元辨識率。

關鍵字

車牌辨識系統 ； 深度學習 ； 卷積類神經網路 ； 智慧城市

並列摘要

Due to the need to detect and track stolen and criminal vehicles and traffic monitoring, the development of license plate recognition systems on intersection monitors system is very urgent for the development of smart cities. Unlike the traditional license plate recognition technology applied to smart parking lots to identify a single license plate in a single lane, license plate recognition applied to intersection monitors must detect multiple license plates on multiple lanes. In addition, license plate recognition applied to intersection monitors faces many challenges, including too small license plates in the picture, unstable light sources, different shooting angles, blurred license plate characters in moving vehicles, and complex road conditions, advertising signs, traffic signs and road name indicator.

To solve the above problems, this paper proposes a new two-stage methodology based on deep learning technology which first detects all the license plates in a picture and extracts the license plate images, and then performs character recognition on the license plate images using Convolutional Neural Networks. Through the two-stage approach, this method increases the proportion of characters in the picture, which in turn improves the character recognition accuracy. Experimental results show that the methodology achieves 98.14% of license plate detection rate and 97.37% of character recognition rate. The performance of the hierarchical methodology is about 23.81 fps.

In addition, we use the AOLP dataset to test. The testing results show that the proposed method has higher license plate detection rate and character recognition rate in license plate detection and character recognition than other methods.

並列關鍵字

License plate recognition system ； deep learning ； Convolutional Neural Networks ； smart city

參考文獻

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