基于BP神经网络的车型识别外文翻译.docx

1、基于BP神经网络的车型识别外文翻译一、外 文 资 料License Plate Recognition Based On Prior KnowledgeAbstract - In this paper, a new algorithm based on improved BP (back propagation) neural network for Chinese vehicle license plate recognition (LPR) is described. The proposed approach provides a solution for the vehicle lic

2、ense plates (VLP) which were degraded severely. What it remarkably differs from the traditional methods is the application of prior knowledge of license plate to the procedure of location, segmentation and recognition. Color collocation is used to locate the license plate in the image. Dimensions of

3、 each character are constant, which is used to segment the character of VLPs. The Layout of the Chinese VLP is an important feature, which is used to construct a classifier for recognizing. The experimental results show that the improved algorithm is effective under the condition that the license pl

4、ates were degraded severely.Index Terms - License plate recognition, prior knowledge, vehicle license plates, neural network.I. INTRODUCTIONVehicle License-Plate (VLP) recognition is a very interesting but difficult problem. It is important in a number of applications such as weight-and-speed-limit,

5、 red traffic infringement, road surveys and park security 1. VLP recognition system consists of the plate location, the characters segmentation, and the characters recognition. These tasks become more sophisticated when dealing with plate images taken in various inclined angles or under various ligh

6、ting, weather condition and cleanliness of the plate. Because this problem is usually used in real-time systems, it requires not only accuracy but also fast processing. Most existing VLP recognition methods 2, 3, 4, 5 reduce the complexity and increase the recognition rate by using some specific fea

7、tures of local VLPs and establishing some constrains on the position, distance from the camera to vehicles, and the inclined angles. In addition, neural network was used to increase the recognition rate 6, 7 but the traditional recognition methods seldom consider the prior knowledge of the local VLP

8、s. In this paper, we proposed a new improved learning method of BP algorithm based on specific features of Chinese VLPs. The proposed algorithm overcomes the low speed convergence of BP neural network 8 and remarkable increases the recognition rate especially under the condition that the license pla

9、te images were degrade severely.II. SPECIFIC FEATURES OF CHINESE VLPSA. DimensionsAccording to the guideline for vehicle inspection 9, all license plates must be rectangular and have the dimensions and have all 7 characters written in a single line. Under practical environments, the distance from th

10、e camera to vehicles and the inclined angles are constant, so all characters of the license plate have a fixed width, and the distance between the medium axes of two adjoining characters is fixed and the ratio between width and height is nearly constant. Those features can be used to locate the plat

11、e and segment the individual character.B. Color collocation of the plateThere are four kinds of color collocation for the Chinese vehicle license plate .These color collocations are shown in table I. TABLE ICategory of license plateColor collocationsmall horse power plateblue background and white ch

12、aractersmotor truck plateyellow background and black charactersmilitary vehicle and police wagon plateblack background and the white charactersembassy vehicle platewhite background and black charactersMoreover, military vehicle and police wagon plates contain a red character which belongs to a speci

13、fic character set. This feature can be used to improve the recognition rate.C. Layout of the Chinese VLPSThe criterion of the vehicle license plate defines the characters layout of Chinese license plate. All standard license plates contain Chinese characters, numbers and letters which are shown in F

14、ig.1. The first one is a Chinese character which is an abbreviation of Chinese provinces. The second one is a letter ranging from A to Z except the letter I. The third and fourth ones are letters or numbers. The fifth to seventh ones are numbers ranging from 0 to 9 only. However the first or the sev

15、enth ones may be red characters in special plates (as shown in Fig.1). After segmentation process the individual character is extracted. Taking advantage of the layout and color collocation prior knowledge, the individual character will enter one of the classes: abbreviations of Chinese provinces se

16、t, letters set, letters or numbers set, number set, special characters set.(a)Typical layout(b) Special characterFig.1 The layout of the Chinese license plateIII. THE PROPOSED ALGORITHMThis algorithm consists of four modules: VLP location, character segmentation, character classification and charact

17、er recognition. The main steps of the flowchart of LPR system are shown in Fig. 2. Firstly the license plate is located in an input image and characters are segmented. Then every individual character image enters the classifier to decide which class it belongs to, and finally the BP network decides

18、which character the character image represents.Image acquisitionPlate locationCharacters segmentation segmentationclassifierChinese characterLetterLetter or numberNumberSpecial characterCharacters recognitionFig.2 The flowchart of LPR systemA. Preprocessing the license plate1) VLP LocationThis proce

19、ss sufficiently utilizes the color feature such as color collocation, color centers and distribution in the plate region, which are described in section II. These color features can be used to eliminate the disturbance of the fake plates regions. The flowchart of the plate location is shown in Fig.

20、3.Characters edge detectionBinary image segmentingCandidate image detectionVehicle plate extractionFig.3 The flowchart of the plate location algorithmThe regions which structure and texture similar to the vehicle plate are extracted. The process is described as followed: (1) (2)Here, the Gaussian va

21、riance is set to be less than W/3 (W is the character stroke width), so gets its maximum value M at the center of the stroke. After convolution, binarization is performed according to a threshold which equals T * M (T MaxFFor each character segmentsCalculate the medium point For each two consecutive

22、 medium pointsCalculate the distance Calculate the minimum distanceMerge the character segment k and the character segment k +1NF = NF - 1End of algorithm where Nf is the number of character segments, MaxF is the number of the license plate, and i is the index of each character segment.The medium po

23、int of each segmented character is determined by: （3）where is the initial coordinates for the character segment, and is the final coordinate for the character segment. The distance between two consecutive medium points is calculated by: （4）Fig.6 The segmentation resultsB. Using specific prior knowle

24、dge for recognition The layout of the Chinese VLP is an important feature (as described in the section II), which can be used to construct a classifier for recognizing. The recognizing procedure adopted conjugate gradient descent fast learning method, which is an improved learning method of BP neura

25、l network10. Conjugate gradient descent, which employs a series of line searches in weight or parameter space. One picks the first descent direction and moves along that direction until the minimum in error is reached. The second descent direction is then computed: this direction the “conjugate dire

26、ction” is the one along which the gradient does not change its direction will not “spoil” the contribution from the previous descent iterations. This algorithm adopted topology 625-35-N as shown in Fig. 7. The size of input value is 625 (25*25 ) and initial weights are with random values, desired output values have the same feature with the input values.Fig. 7 The network topologyAs Fig. 7 shows, there is a three-l