臺灣博碩士論文加值系統

在車輛相關的犯罪調查中車牌已被認定為最重要的資訊，例如：贓車查緝等。以往傳統的調查案件必須透過調查員(警察)手動輸入可疑車輛的車牌號碼至贓車系統，此步驟不僅耗費人力而且制式化的動作使人繁瑣乏味。本研究考量行車紀錄器的錄影功能，以及巡邏車(警車)取締贓車的資訊，提出一套「巡邏車輛之自動車牌辨識系統」，主要是根據巡邏車針對路邊停車之車輛進行自動化的車牌辨識，本系統分為硬體配置及軟體開發兩個部分，並針對這兩個部分進行詳細的說明。技術方面包含：車牌定位、車牌校正、字元切割和字元辨識，根據這些技術本系統在車牌定位和字元辨識上，不管是車牌偵測率和字元辨識率皆可達90%以上。總而言之，本系統可以結合無線通訊技術查詢車輛的相關資訊，並且協助關於車輛的犯罪案件進行調查。

License plates are considered the first important information for vehicle-related crime investigation (e.g., vehicle theft, etc.). Conventional investigation requires the investigator (policeman) to manually enter the license plate number for suspicious vehicles which remains tedious and labor-intensive. The objective of this study was to develop an Automatic license plate recognition system for patrolling vehicles. We explored the idea to provide an automatic system by installing a surveillance camera (e.g., a vehicle video recorder) on a patrolling vehicle (e.g., police car). The system can be described in two phases, namely hardware configuration and software development. Technical approaches included: License Plate Localization, License Plate Correction, Character Segmentation, and Character Recognition. Overall, our system could achieve the license plate localization and character recognition of over 90%. In summary, our system could be incorporated in an integrated system with wireless communication for querying the vehicles’ information to assist the vehicle-related crime investigation.

目錄
摘要...........................................................................I
Abstract......................................................................II
誌謝.........................................................................III
目錄..........................................................................IV
圖索引........................................................................VI
表索引........................................................................IX
第一章 緒論...................................................................1
1.1 研究背景...............................................................1
1.2 相關研究...............................................................2
1.3 研究動機與目的.........................................................6
1.4 論文架構...............................................................7
第二章 研究方法...............................................................8
2.1 硬體配置...............................................................8
2.2 軟體發展...............................................................9
2.3 車牌定位..............................................................11
2.2.1 灰階轉換..............................................................11
2.2.2 直方圖等化............................................................12
2.2.3 中值濾波器............................................................14
2.2.4 Sobel邊緣偵測.........................................................17
2.2.5 Otsu二值化............................................................18
2.2.6 形態學處理............................................................21
2.2.7 特徵擷取..............................................................23
2.4 車牌校正..............................................................25
2.3.1 Otsu/Bernsen二值化....................................................26
2.3.2 車牌傾斜校正..........................................................28
2.5 字元切割..............................................................35
2.4.1 垂直投影..............................................................36
2.4.2 水平投影..............................................................37
2.6 字元辨識..............................................................38
2.5.1 正規化................................................................39
2.5.2 樣板比對..............................................................39
第三章 研究結果..............................................................43
3.1 研究環境與設備........................................................43
3.2 研究結果展示..........................................................44
第四章 討論與未來展望........................................................52
參考文獻......................................................................54
 
圖索引
圖2-1 系統硬體架設示意圖.....................................................8
圖2-2 「巡邏車輛之自動車牌辨識系統」流程圖..................................10
圖2-3 彩色影像轉灰階影像範例圖..............................................11
圖2-4 灰階影像轉直方圖等化影像範例圖........................................13
圖2-5 5x4影像...............................................................14
圖2-6 九宮格像素重新排序(一)................................................15
圖2-7 取代後的5x4影像(一)...................................................15
圖2-8 九宮格像素重新排序(二)................................................15
圖2-9 取代後的5x4影像(二)...................................................15
圖2-10 中值濾波後5x4影像.....................................................16
圖2-11 直方圖等化影像使用中值濾波器範例圖....................................16
圖2-12 Sobel遮罩.............................................................17
圖2-13 Sobel邊緣偵測範例圖...................................................18
圖2-14 Otsu二值影像範例圖....................................................20
圖2-15 形態學處理膨脹和侵蝕示意圖............................................21
圖2-16 形態學處理斷開及閉合示意圖............................................22
圖2-17 形態學處理範例圖......................................................23
圖2-18 車牌特徵擷取範例圖....................................................24
圖2-19 車牌校正子流程圖......................................................25
圖2-20 Otsu/Bernsen二值化範例圖..............................................27
圖2-21 字元偵測繪製直角三角形範例圖..........................................28
圖2-22 直角三角形示意圖......................................................29
圖2-23 雙線性內插法示意圖....................................................30
圖2-24 車牌旋轉校正範例圖....................................................31
圖2-25 字元垂直投影水平校正流程圖............................................32
圖2-26 車牌垂直投影範例圖....................................................33
圖2-27 車牌字元及山丘個數範例圖..............................................33
圖2-28 車牌字元旋轉水平校正範例圖............................................34
圖2-29 字元切割子流程圖......................................................35
圖2-30 垂直投影切割範例圖....................................................36
圖2-31 水平投影切割範例圖....................................................37
圖2-32 字元辨識子流程圖......................................................38
圖2-33 字元正規化範例圖......................................................39
圖2-34 新舊式車牌字元樣板資料庫範例圖........................................40
圖2-35 字元區域樣板比對範例圖................................................41
圖2-36 字元辨識範例圖........................................................42
圖3-1 自動車牌辨識系統研究結果圖(一)........................................44
圖3-2 自動車牌辨識系統研究結果圖(二)........................................45
圖3-3 自動車牌辨識系統研究結果圖(三)........................................45
圖3-4 自動車牌辨識系統研究結果圖(四)........................................46
圖3-5 自動車牌辨識系統研究結果圖(五)........................................46
圖3-6 自動車牌辨識系統研究結果圖(六)........................................47
圖3-7 自動車牌辨識系統研究結果圖(七)........................................47
圖3-8 自動車牌辨識系統研究結果圖(八)........................................48
圖3-9 自動車牌辨識系統研究結果圖(九)........................................48
圖3-10 車牌定位及字元辨識失敗結果圖..........................................50
 
表索引
表2-1 XOR真值表.............................................................40
表3-1 系統研究環境與設備....................................................43
表3-2 車牌定位偵測率及字元辨識率............................................49
表3-3 車牌定位及字元辨識比較表..............................................51

[1]P. Rattanathammawat and T. H. Chalidabhongse, “A Car Plate Detector using Edge Information,” Proceeding of the IEEE International Symposium on Communications and Information Technologies (ISCIT), pp. 1039-1043, 2006.

[2]B. Chen, W. Cao and H. Zhang, “An Efficient Algorithm on Vehicle License Plate Location,” Proceeding of the IEEE International Conference on Automation and Logistics Qingdao, pp. 1386-1389, September 2008.

[3]X. He, H. Zhang, W. Jia, Q. Wu and T. Hintz, “Combining Global and Local Features for Detection of License Plates in a Video,” Proceedings of Image and Vision Computing New Zealand, pp. 288-293, December 2007.

[4]L. W. Ju, L. D. Qun, C. L. Yan and W. X. Nian, “A Novel Approach for Vehicle License Plate Tilt Correction,” Information and Control, vol. 33, no. 2, pp. 231-235, April 2004.

[5]W. Mei and W. G. Hong, “Method of Vehicle License Plate Correction Based on Characters Projection Minimum Distance,” Computer Engineering, vol. 34, no. 6, pp. 216-218, March 2008.

[6]J. M. Guo and Y. F. Liu, “License Plate Localization and Character Segmentation With Feedback Self-Learning and Hybrid Binarization Techniques,” IEEE Transactions on Vehicular Technology, vol. 57, no. 3, pp. 1417-1424, May 2008.

[7]X. Zhang, X. Liu and H. Jiang, “A Hybrid Approach to License Plate Segmentation under Complex Conditions,” Third International Conference on Natural Computation (ICNC), pp. 68-73, August 2007.

[8]F. Yang, Z. Ma and M. Xie, “A Novel Approach for License Plate Character Segmentation,” IEEE Conference on Industrial Electronics and Applications (ICIEA), pp. 1-6, May 2006.

[9]L. Angeline, W. Y. Kow, W. L. Khong, M. Y. Choong and K. T. K. Teo, “License Plate Character Recognition via Signature Analysis and Features Extraction,” Fourth International Conference on Computational Intelligence, Modelling and Simulation (CIMSiM), pp. 1-6, September 2012.

[10]P. Hidayatullah, N. Syakrani, I. Suhartini and W. Muhlis, “Optical Character Recognition Improvement for License Plate Recognition in Indonesia,” UKSim-AMSS 6th European Modelling Symposium on Computer Modeling and Simulation (EMS), pp. 249-254, November 2012.

[11]C. H. Lee, K. L. You and Y. P. Lin, “Dynamic Real-Time License Plate Recognition,” Journal of Technology, vol. 25, no. 2, pp. 151-165, 2010.

[12]N. Otsu, “A Threshold Selection Method from Gray-Level Histograms,” IEEE Transactions on Systems, Man and Cybernetics, vol. SMC-9, no. 1, pp. 62-66, January 1979.

[13]R. C. Gonzalez and R. E. Woods, Digital Image Processing, 3rd Edition, Pearson Education, Inc., New Jersey, 2008.

[14]Y. Wen, Y. Lu, J. Yan, Z. Zhou, K. M. Deneen and P. Shi, “An Algorithm for License Plate Recognition Applied to Intelligent Transportation System,” IEEE Transactions on Intelligent Transportation Systems, vol. 12, no. 3, pp. 830-845, September 2011.

[15]T. R. Singh, S. Roy, O. I. Singh, T. Sinam and K. M. Singh, “A New Local Adaptive Thresholding Technique in Binarization,” International Journal of Computer Science Issues (IJCSI), vol. 8, Issue 6, no. 2, pp. 271-277, November 2011.

[16]E. N. Kaur and E. R. Kaur, “A Review on Various Methods of Image Thresholding,” International Journal on Computer Science and Engineering (IJCSE), vol. 3, no. 10, pp. 3441-3443, October 2011.

[17]中華民國車輛牌照http://zh.wikipedia.org/wiki/%E8%87%BA%E7%81%A3%E8%BB%8A%E8%BC%9B%E7%89%8C%E7%85%A7

[18]R. K. Megalingam, P. Krishna, P. Somarajan, V. A. Pillai and R. U. Hakkim, “Extraction of License Plate Region in Automatic License Plate Recognition,” International Conference on Mechanical and Electrical Technology (ICMET), pp. 496-501, September 2010.

[19]N. Vishwanath, S. Somasundaram, A. Nishad and N. K. Nallaperumal, “Indian License Plate Character Recognition using Kohonen Neural Network,” IEEE International Conference on Computational Intelligence and Computing Research (ICCIC), pp. 1-4, December 2012.

[20]C. C. Hsieh and C. C. Chang, “License Plate Detection using Hough Transform and Haar-like Cascade Classifiers,” Journal of Information, Technology and Society, pp. 21-33, 2013.

[21]K. Deb and K. H. Jo, “HIS Color based Vehicle License Plate Detection,” International Conference on Control, Automation and Systems, pp. 687-691, October 2008.

[22]C. N. E. Anagnostopoulos, I. E. Anagnostopoulos, V. Loumos and E. Kayafas, “A License Plate-Recognition Algorithm for Intelligent Transportation System Applications,” IEEE Transactions on Intelligent Transportation Systems, vol. 7, no. 3, pp. 377-392, September 2006.

[23]H. A. Hegt, R. J. D. Haye and N. A. Khan, “A High Performance License Plate Recognition System,” IEEE International Conference on Systems, vol. 5, pp. 4357-4362, October 1998.