林政宏Lin, Cheng-Hung林永鑫Lin, Yong-Sin2019-09-032018-08-082019-09-032018http://etds.lib.ntnu.edu.tw/cgi-bin/gs32/gsweb.cgi?o=dstdcdr&s=id=%22G060575005H%22.&%22.id.&http://rportal.lib.ntnu.edu.tw:80/handle/20.500.12235/95688近年來，車牌辨識系統已成為智能城市車輛管理、被盜車輛調查、交通監控等發展中的關鍵角色，車牌辨識系統有三個階段，包括車牌偵測、字元分割，與字元辨識。儘管車牌辨識系統已成功的應用於環境單純的智能停車場，但使用於監控系統中仍會面臨許多問題，例如多車道辨識，大量的交通號誌與廣告招牌，惡劣天氣與夜間拍攝的模糊傾斜圖像。本論文提出了一種高效的車牌辨識系統，首先偵測車輛，再從車輛中偵測車牌，以減少車牌偵測的誤報。再使用卷積神經網路來改善模糊圖像與近似字元的辨識效果，實驗結果顯示，與傳統的車牌辨識系統相比，該系統擁有較高的精確度。In recent years, license plate recognition system has become a crucial role in the development of smart cities for vehicle management, investigation of stolen vehicles, and traffic monitoring and control. License plate recognition system has three stages, including license plate localization, character segmentation, and character recognition. Up to now the license plate recognition system has been successfully applied to the environment-controlled smart parking system, however it still raises many challenges in the surveillance system such as congested traffic with multiple plates, ambiguous signs and advertisements, tilting plates, as well as obscure images that are captured during bad weather and poor light conditions. In this thesis, we propose an efficient license plate recognition system that first detects vehicles and then retrieves license plates from the detected vehicles to reduce false positives on plate detection. Thereafter, the technique of convolution neural networks is applied to improve the character recognition accuracy from the blurred and obscure images. The experimental results show the superiority of the performance in the proposed method as compared to the traditional license plate recognition systems.車牌辨識系統卷積神經網路智慧都市License plate recognition systemconvolution neural networksmart city