Approaches for image processing in the decision support system of the center for automated recording of administrative offenses of the road traffic

Minnikhanov R.N., Anikin I.V., Dagaeva M.V., Asliamov T.I., Bolshakov T.E.
 pdf (1440K)

We suggested some approaches for solving image processing tasks in the decision support system (DSS) of the Center for Automated Recording of Administrative Offenses of the Road Traffic (CARAO). The main task of this system is to assist the operator in obtaining accurate information about the vehicle registration plate and the vehicle brand/model based on images obtained from the photo and video recording systems. We suggested the approach for vehicle registration plate recognition and brand/model classification on the images based on modern neural network models. LPRNet neural network model supplemented by Spatial Transformer Layer was used to recognize the vehicle registration plate. The ResNeXt-101-32x8d neural network model was used to classify for vehicle brand/model. We suggested the approach to construct the training set for the neural network of vehicle registration plate recognition. The approach is based on computer vision methods and machine learning algorithms. The SIFT algorithm was used to detect and describe local features on images with the vehicle registration plate. DBSCAN clustering was used to detect and delete outliers in such local features. The accuracy of vehicle registration plate recognition was 96% on the testing set. We suggested the approach to improve the efficiency of using the ResNeXt-101-32x8d model at additional training and classification stages. The approach is based on the new architecture of convolutional neural networks with “freezing” weight coefficients of convolutional layers, an additional convolutional layer for parallelizing the classification process, and a set of binary classifiers at the output. This approach significantly reduced the time of additional training of neural network when new vehicle brand/model classification was needed. The final accuracy of vehicle brand/model classification was 99% on the testing set. The proposed approaches were tested and implemented in the DSS of the CARAO of the Republic of Tatarstan.

Keywords: decision-support system, video image, computer vision, neural networks
Citation in English: Minnikhanov R.N., Anikin I.V., Dagaeva M.V., Asliamov T.I., Bolshakov T.E. Approaches for image processing in the decision support system of the center for automated recording of administrative offenses of the road traffic // Computer Research and Modeling, 2021, vol. 13, no. 2, pp. 405-415
Citation in English: Minnikhanov R.N., Anikin I.V., Dagaeva M.V., Asliamov T.I., Bolshakov T.E. Approaches for image processing in the decision support system of the center for automated recording of administrative offenses of the road traffic // Computer Research and Modeling, 2021, vol. 13, no. 2, pp. 405-415
DOI: 10.20537/2076-7633-2021-13-2-405-415
URL: http://crm-en.ics.org.ru/journal/article/3061/
Creative Commons License This work is licensed under a Creative Commons Attribution-NoDerivs 3.0 Unported License.

Indexed in Scopus

Full-text version of the journal is also available on the web site of the scientific electronic library eLIBRARY.RU

The journal is included in the Russian Science Citation Index

The journal is included in the RSCI

International Interdisciplinary Conference "Mathematics. Computing. Education"

Copyright © 2009–2024 Institute of Computer Science