Fake plate detection method based on license plate identification and vehicle feature matching

Abstract

The invention discloses a fake plate detection method based on license plate identification and vehicle feature matching. The method comprises the following steps that: extracting a monitoring equipment frame image, and carrying out graying on a source image; adopting Sobel edge detection to position a license plate; adopting a morphological processing image to enable regions to be communicated soas to bring convenience for extracting the outline of the license plate; setting an aspect ratio to accurately extract areas; through hough transformation and vertical projection, carrying out license plate correction and character segmentation; using a neural network to identify segmented characters to obtain license plate information; migrating an AlexNet neural network frame, and carrying outclassification through the identification of the depth feature of the color; and applying a KNN (K-Nearest Neighbor) algorithm to be combined with database system information to detect a fake plate situation. By use of the method, vehicle identification accuracy is guaranteed, a high-accuracy convolutional neural network is directly migrated to serve as a basic framework, cost and expenditure aresmall, the method can be quickly realized on a computer platform, cost is small for arranging a license plate identification and vehicle identification system on a large scale, and feasibility is high.

Description

technical field [0001] The invention relates to a license plate detection method based on license plate recognition and vehicle feature matching. On the basis of license plate recognition, combined with transfer learning, the vehicle information can be recognized more effectively, and abnormal deck detection is completed in combination with a database. Background technique [0002] License plate recognition and vehicle recognition are widely used in the field of intelligent transportation, such as toll stations, parking lot management, investigation and punishment of illegal vehicles, etc. Domestic license plates are composed of Chinese characters, letters, and numbers. Due to the complexity of Chinese characters, it is much more difficult to recognize than letters and numbers. The various characteristics of license plate color, format, and suspension mode, the intensity of light, the degree of reflection of the license plate, the appearance of a rectangular area similar to ...

AI Technical Summary

Problems solved by technology

The variety of license plate colors, formats, and suspension methods, the intensity of light, the degree of reflection of the license plate, the appearance of a rectangular area similar to the license plate in the background, and other external factors and shooting angles also increase the difficulty of license plate recognition.

At present, the main methods of license plate recognition are texture features and color matching, which enhance the reliability of the system, but the algorithm is more complicated

The color of the license plate is similar to that of the body of the neural network; when the license plate is stained, the syntactic features of the license plate domain change, it is difficult to accurately locate the license plate, and the robustness is not strong

Due to the introduction of differential operations, traditional edge detection is sensitive to noise and has poor anti-noise performance

Directly using the Gaussian function to process the original image requires a lot of calculation

The recognition of vehicles mainly adopts the method of SIFT feature vector collection. This method is not real-time, sometimes there are few feature points, and the feature points cannot be accurately extracted for objects with smooth edges.

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