以雙核心處理器為基礎之車牌辨識系統 ; A Based on Dual-Core Processor License Plate Recognition System

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题名:	以雙核心處理器為基礎之車牌辨識系統;A Based on Dual-Core Processor License Plate Recognition System
作者:	林立;Li Lin
贡献者:	資訊工程研究所
关键词:	車牌辨識;OMAP3530;車牌定位;異質雙核心處理器;嵌入式系統;兩階段式神經網路分類器;heterogeneous dual-core processors;embedded system;two-stage neural network classifier;license plate location;OMAP3530;license plate recognition
日期:	2010-07-09
上传时间:	2010-12-09 13:49:48 (UTC+8)
出版者:	國立中央大學
摘要:	本研究目的在於發展一個高辨識率且低複雜度的嵌入式車牌辨識系統。本論文同時在桌上型PC以及ARM-cortex A8上面進行實驗，系統包含三個主要模組: 車牌定位、車牌字元旋轉與切割、車牌字元辨識。車牌定位是先使用小波轉換後，做邊緣偵測來找出所有可能的車牌區域；車牌字元切割是將定位出來的車牌區域，依據連通物件以及車牌字元的特性，加以分析處理，將切割出來的車牌字元旋轉至水平，並且切割出車牌字元；最後，我們提出一個兩階段式神經網路分類器(two-stage neural network classifier)來進行字元辨識。採用適應性的車牌定位方法，在複雜和持續變動的真實環境下，我們的車牌辨識系統仍擁有高度分割出車牌字/以及背景的正確率；另外，在真實應用中，車牌影像資料總是隨著時間不斷加入，新增的車牌影像品質和特性無法以事前的批次學習方式掌握，本研究所設計的兩階段式神經網路分類器，可改善一般的分類器訓練過後無法繼續學習的缺點，得以在變動的環境下，動態調整分類器架構和參數最佳化，以維持最佳的辨識性能。我們首先於PC進行演算法的驗證和實驗，繼而將系統移植到異質雙核心處理器的嵌入式平台。我們將分析車牌辨識系統各模組的複雜度，將計算複雜度較高的模組移到DSP處理器上處理並與ARM處理器進行協同運算，最後實現基於雙核心架構的車牌辨識嵌入式軟體。實驗顯示，與其他車牌辨識研究比較，我們的系統具有優異的辨識性能和效率。This paper aims to establish a high recognition rate and low complexity embedded license plate recognition system. Our system consists of three main parts, including license plate location, segmentation of characters and characters recognition. The license plate locations in image are identified by wavelet transform and edge detection. The segmentation of characters uses connected-component and the feature of license plate of characters to segment each one. Character recognition is achieved with two stage neural network classifier. We use adaptive method in License plate location. Our system still has a high correct rate of dividing the license plate and the background in complex and always changing environment. Besides, in real applications, license plate images are always being added over time, the new license plate image quality and features can’t be controlled in previous training. In this research, the two stage neural network classifier can improve the defects of the general classifiers that can’t continue learning after learning. It can dynamic adjustment of classifier structure and parameters optimization to maintain the best recognition performance in the changing environment. At first, we have verified our algorithm and experimented on PC; after that, our license plate recognition system has migrated to the heterogeneous dual-core processors embedded system. We have analyzed each module’s complexity in our license plate recognition system, moved higher computation functions to DSP processor and collaborative computing with the ARM processor. Finally we implemented the embedded software based on dual-core architecture. Experiments show that our system has excellent performance and efficiency of identification than other license plate recognition researches.
显示于类别:	[資訊工程研究所] 博碩士論文

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