本文關鍵詞：基于深度學習的車牌識別技術研究　出處：《青島科技大學》2017年碩士論文　論文類型：學位論文

  更多相關文章： 車牌定位 車牌識別 深度學習 目標檢測 卷積神經(jīng)網(wǎng)絡

【摘要】：車牌識別是智能交通系統(tǒng)中極為重要的一部分,在智慧城市的理念當中也不可或缺,具有較高的研究與應用價值。雖然經(jīng)過長時間的研究和努力,我國的車牌識別技術也取得了優(yōu)秀的研究成果,能夠解決一般場景下的車牌識別問題,如交通固定卡口、車庫出入口和小區(qū)門禁等場景。但在自然場景下出現(xiàn)的車牌傾斜、車牌扭曲、光照條件較差、像素分辨率較低等情況,不能夠準確的進行車牌定位與識別。針對這些問題,本文使用基于深度學習的目標檢測方法進行以下幾個方面的研究。首先,本文提出使用基于深度學習的目標檢測方法對車牌進行定位,并對目標檢測的卷積神經(jīng)網(wǎng)絡結構進行改進。在模型的訓練過程中,使用圖像標注軟件對現(xiàn)場采集到的30521張圖片進行手工標注。為了增加訓練樣本的數(shù)量,隨機對圖像進行鏡像和縮放等操作。使用訓練好的模型在多個不同交通卡口采集的數(shù)據(jù)進行測試。將測試結果與使用灰度圖像進行車牌定位的方法進行比較,驗證了基于深度學習的目標檢測方法在車牌定位方面的魯棒性。其次,同樣使用基于深度學習的目標檢測方法對車牌字符進行檢測,并針對內(nèi)地車牌和港澳臺車牌對檢測結果分別進行相對應的排序處理,最終得到車牌識別結果。在模型的訓練過程中,本文使用圖像標注軟件對21269張圖片進行手工標注。由于車牌中的字符具有不對稱性,則不對圖像做增強處理。使用不同的網(wǎng)絡結構對該數(shù)據(jù)集進行訓練、測試,并使用現(xiàn)場采集到的圖像數(shù)據(jù)進行對比驗證,選取最優(yōu)網(wǎng)絡模型。最后將車牌定位和車牌識別過程相結合,統(tǒng)一測試整個系統(tǒng)的識別速度。測試結果顯示識別速度為每張圖片0.12秒,比傳統(tǒng)的車牌識別系統(tǒng)有很大的優(yōu)勢。另外該系統(tǒng)可以與車輛檢測、車型識別完美結合。待檢測圖片先進行車輛檢測和車型識別,再進行車牌的檢測與識別。這些結果可以通過搭建大數(shù)據(jù)平臺,實時上傳檢測到車輛的車型、車牌和位置等信息,對建設智慧城市、實現(xiàn)萬物互聯(lián)具有重大意義。
[Abstract]:License plate recognition is one of the most important part of the intelligent transportation system, is indispensable in the concept of smart city, has a high value of research and application. After long time research and efforts, the license plate recognition technology in China has also made outstanding research results, can solve the problem of general license plate recognition scenarios. Such as traffic fixed bayonet, garage entrance and residential access scenarios. But the license plate appears in natural scene tilt, plate distortion, poor light condition, low resolution pixel, can not carry out license plate location and recognition accuracy. Aiming at these problems, this paper uses the following research methods to detect deep learning based on the target. First of all, the paper proposes using the detection method of deep learning targets based on the license plate location, network structure and convolution neural network on target detection of The improvement in the training process. In the model, 30521 images using image annotation software to the collection of field of manual annotation. In order to increase the number of training samples, random image and zoom the image. Using the trained model was tested in a number of different traffic stations collected data. Compare the method of license plate the positioning will test results with the use of gray image, to verify the detection method of deep learning goals based on the license plate location robustness. Secondly, using the same method to detect deep learning targets based on the license plate characters were detected, and the mainland and Hong Kong and Macao on license plate license plate detection results respectively corresponding to the sorting, finally get the license plate recognition results. In the training process of the model, this paper uses the image of 21269 images were manually labeling software. Due to the license plate The asymmetry of the character has no image enhancement processing. Using different network structure of the data set for training, testing, and comparing the results using the image data collected, selecting the optimal network model. Finally, the license plate location and license plate recognition process combining unified testing of the whole system test the recognition speed. The results show that the recognition speed of 0.12 seconds for each picture, it has more advantages than the traditional license plate recognition system. This system also can be combined with vehicle detection, vehicle recognition. Perfect detecting image to vehicle detection and vehicle recognition, then the license plate detection and recognition. These results can build a big data platform, real-time upload to the vehicle license plate detection models, and location information, for the construction of smart city, to achieve interconnection of all things is of great significance.

【學位授予單位】：青島科技大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：U495;TP391.41

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