本文關(guān)鍵詞：BP神經(jīng)網(wǎng)絡(luò)聯(lián)合模板匹配的車(chē)牌識(shí)別系統(tǒng)，由筆耕文化傳播整理發(fā)布。

BP神經(jīng)網(wǎng)絡(luò)聯(lián)合模板匹配的車(chē)牌識(shí)別系統(tǒng)

License plate recognition system using a BP neural network and template matching

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GUO Runhua, SU Tingting, MA Xiaowei （Department of Civil Engineering, Tsinghua University, Beijing 100084, China）

清華大學(xué)土木工程系,北京100084

文章摘要：車(chē)牌識(shí)別是智能交通系統(tǒng)的重要部分，對(duì)實(shí)時(shí)自動(dòng)監(jiān)控具有重要的意義。用科學(xué)的方法識(shí)別字符，同時(shí)提高識(shí)別準(zhǔn)確率是改善車(chē)牌識(shí)別系統(tǒng)的核心問(wèn)題。由于采集的車(chē)牌圖像會(huì)存在噪音和干擾，，現(xiàn)有方法存在識(shí)別效率低下的問(wèn)題。本文基于Matlab平臺(tái)，聯(lián)合應(yīng)用BP（backpropagation）神經(jīng)網(wǎng)絡(luò)和模板匹配方法優(yōu)化車(chē)牌字符識(shí)別方法，在神經(jīng)網(wǎng)絡(luò)收斂的情況下嵌入模板匹配方法精確識(shí)別。針對(duì)車(chē)牌的特點(diǎn)提出一種高效的神經(jīng)網(wǎng)絡(luò)字符特征提取方法，從單字符的800個(gè)像素特征中僅提取211個(gè)特征向量。該方法識(shí)別率高（97．2％）、識(shí)別時(shí)間短（單字符0．02S和全車(chē)牌0．39s）、抗干擾和容錯(cuò)性強(qiáng)。在理論創(chuàng)新的基礎(chǔ)上設(shè)計(jì)了LPR2011車(chē)牌識(shí)別系統(tǒng)，可應(yīng)用于實(shí)際交通管制。

Abstr：License plate recognition is an important part of intelligent transportation systems with great significance for real time automatic road monitoring. The recognition accuracy rate is the core issue of the system. Existing methods are not effective due to noise and interference. This research describes license plate recognition method that combines a BP （back propagation） neural network and template matching. The method embeds template matching in the convergent neural network for further identification. The algorithm uses an efficient feature extraction method for the neural network which extracts only 211 features out of 800 pixels. The method achieves a high recognition rate of 97.2~ with rapid processing （0.02 s for single character and 0.39 s for an entire license plate）, resists interference and as tolerant to various faults. The system can be applied to actual traffic control.

文章關(guān)鍵詞：

Keyword：:traffic control license plate recognition character recognition BP （back propagation） neural network template matching feature extraction

課題項(xiàng)目：國(guó)家自然科學(xué)基金資助項(xiàng)目（51008169）

作者信息：會(huì)員可見(jiàn)

  本文關(guān)鍵詞：BP神經(jīng)網(wǎng)絡(luò)聯(lián)合模板匹配的車(chē)牌識(shí)別系統(tǒng)，由筆耕文化傳播整理發(fā)布。