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A Robust Approach for Licence Plate Detection Using Deep Learning
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AI
DOI：10.4114/intartif.vol27iss73pp129-141.png)
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摘要
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Intelligent transport systems must be developed due to the rising use of vehicles, particularly cars. In the field of computer vision, the identification of a vehicle's license plate (LP) has been crucial. Various methods and algorithms have been used for the detection process. It becomes challenging to find similar images, nevertheless, because the features of these plates change depending on the characters' color, font, and language. The research proposes a robust deep-learning framework based on feature extraction using convolutional neural networks and localization using an improved approach integrating bilateral filtering and Canny edge detection. Further, a CNN architecture is used to extract features from images and classify the presence of license plates in unseen vehicles. If present, the stage is followed by recognition of numbers written on the plates. An extensive experimental investigation is performed on Stanford Cars, Car Licence Plate Detection dataset, and Indian Licence Plates Dataset. The attained simulation outcome ensures a superior performance over existing techniques in a significant way.
Keyword:
Vehicle plates
OpenCV
Deep learning
CNN
Recognition
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