用小腦模型在FPGA上作車牌辨識

張簡子介

指導教授 ： 張吉正

國立臺灣師範大學/科技學院/工業教育學系/碩士(2002年)

摘要

I
摘要
自動化車牌辨識是一套專門辨識與記錄車輛牌照系統，其過程分兩階段，第
一階段之車牌字元擷取是應用圖像灰階化、圖像二值化、交越特性及投影分析等
運算法則找出車牌上6 個字元的位置；第二階段的字元辨識是利用類神經網路或
是樣板比對等方法作每個字元的辨識。
上述車牌字元擷取或字元辨識使用到的各種運算法則皆引伸出大量資料之
重覆運算，目前多使用相關PC 組成之電腦系統作軟體處理，如欲充分發揮車牌
辨識系統的功能應將之小型化或作為可攜式系統，讓它廣泛使用於各種停車場管
理，甚至可輔助相關警政之車輛查緝以方便車輛管理。
FPGA（Field Programmable Logic Array）是將上述軟體程式作成硬體電路小
型化的重要方法。本研究是利用類神經網路中的小腦模型（Cerebellar Model
Articulation controller，CMAC）作成字元辨識的方法，利用FPGA 作成硬體電路
以達到小型化與快速運算的目的。
CMAC 是1975 年Albus 根據Marr 的小腦皮質模型發展出來的數學演算法，
由於沒有涉及到艱深的數學運算與構造簡單，正好適用於作硬體化，且CMAC
學習時具有快速收斂、良好的類化能力（generalization）等優點，故常被應用在
機械手臂控制及機器人步行控制等非線性系統。
在實驗50 張自用小客車的車牌中，使用軟體模擬的車牌辨識系統有5 張車
牌辨識錯誤，車牌辨識率為90%；在50 張車牌的300 個字元中，有5 個字元辨
識錯誤，總字元辨識率為98.3%，辨識錯誤的情形為“0”誤認為“D”及“1”
誤認為“I”；另外，在硬體實驗的車牌辨識率為70%，總字元辨識率為92.3%，
其中“0”字元的辨識率為0%。晶片設計所使用的Gate Count 為11,420。

關鍵字

可程式閘排列（FPGA ； 圖像處理 ； 類神經網路 ； 小腦模型（CMAC）

並列摘要

II
Abstract
Vehicle license plate recognition is a system especially for recognizing and
recording the car license plate. The whole process can be accomplished by two steps
namely, the car plate catching and the character recognition. The first step, or plate
catching step, applies the image processing technologies, such as color graying, image
binarization, edge detection, crossing characteristics, to locate the license plate and the
six characters on the plate from an image picture of a car. The second step, or character
recognition step, applies the technologies such as Neural Network, Template Matching
for pattern recognition to identify each character found from the first step.
The technologies used by plate catching and character recognition are actually
using the algorithms that need large quantity of data transfer and massive repeated
mathematical calculations. Currently the plate recognition system is used mostly in big
parking area or in free way turnpike. They are large PC related compute network
system. If the system can be made small or even portable, then it can be more widely
used in car parking or help in charging of high way car patrolling.
FPGA (Field Programmable Logic Array) is one of the way to have these PC
software become hardware circuits (so called hardwarization). This project is trying to
organize CMAC (Cerebellar Model Articulation controller) algorithm to perform
character recognition, and transfer it into hardware circuit to obtain fast processing and
system minimization.
CMAC is mathematical equations developed by Albus in 1975 based on Mass’s
Cerebellar Cortex. There is no complicated mathematical operation involved in
CMAC. So it is suitable for hardwarization. Also CMAC’s learning demonstrates a
fast convergence and a good generalization capability, so mostly applied to
manipulator and biped walking robot in nonlinear system.
The software experiment shows a license recognition ratio of 90% and a character
recognition ratio of 98.3%. The hardware experiment shows a license recognition ratio
of 70% and a character recognition ratio 92.3%. Gate count of chip design are 11,420.

並列關鍵字

FPGA ； Image Processing ； Neural Network ； CMAC