Two-Step Algorithm for License Plate Identification Using Deep Neural Networks

• Published in: Applied sciences Vol. 13; no. 8; p. 4902
• Main Authors: Kundrotas, Mantas, Janutėnaitė-Bogdanienė, Jūratė, Šešok, Dmitrij
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.04.2023
• Subjects: Algorithms; convolutional neural networks; Datasets; deep learning; image processing; license plate detection and recognition; License plates; Licensing agreements; Machine vision; Methods; Neural networks; vehicle detection; Vehicles
• ISSN: 2076-3417; 2076-3417
• DOI: 10.3390/app13084902

License plate identification remains a crucial problem in computer vision, particularly in complex environments where license plates may be confused with road signs, billboards, and other objects. This paper proposes a solution by modifying the standard car–license plate–letter detection approach into a preliminary license plate detection–precise license plate detection of the four corners where the numbers are located–license plate correction–letter identification. This way, the first algorithm identifies all potential license plates and passes them as input parameters to the next algorithm for more precise detection. The main difference between this approach and other algorithms is that it uses a relatively small image compared to the whole vehicle. Thus, a small but robust network is used to find the four corners and perform a perspective transformation. This simplifies the letter recognition task for the next algorithm, as no additional transformations are required. This solution could be useful for research focusing on this specific task. It allows to apply another compact but robust neural network, increasing the overall speed of the system. Publicly available datasets were used for training and validation. The CenterNet object detection algorithm was used as a basis with a modified Hourglass-type network. The size of the network was decreased by 40% and the average accuracy was 96.19%. Speed significantly increased, reaching 2.71 ms and 405 FPS on average.