Real time license plate number extraction of non-helmet person using YOLO algorithm
Keywords:
YOLO, Helmet detection, LP detection, OCRAbstract
One of the problems in traffic regulations in India is riding motorcycle/mopeds without helmet, which increases accident sand deaths. In the existing system, the traffic police monitor the traffic violations through CCTV recordings, and in case if the rider without helmet is detected, then its vehicle number is recorded. But the constant monitoring is required to control the traffic rule violation which happens very frequently. To overcome these problems, we will require a system which would automatically handle traffic violations for non-helmet rider and thus would automatically extract the vehicles’ license plate number. The various research has successfully done in this area using CNN, R-CNN, LBP, HoG, HaaR features etc., but the results are limited with respect to efficiency, accuracy and speed. To overcome the problems associated with it, we develop a Non-Helmet Rider detection system, which attempts to satisfy the automation of detecting the traffic violation of non-helmet person and extracting the vehicles’ license plate number. The main principle involved in this system is Object Detection using Deep Learning at three levels. The person, motorcycle/moped is detected at first level using YOLOv2, helmet at second level using YOLOv3, License plate at the last levelusing YOLOv2.
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H. Lin, J. D. Deng, D. Albers and F. W. Siebert, "Helmet Use Detection of Tracked Motorcycles Using CNN-Based Multi-Task Learning," in IEEE Access, vol. 8, pp. 162073-162084, 2020, doi: 10.1109/ACCESS.2020.3021357.
Jamtsho, Yonten&Riyamongkol, Panomkhawn&Waranusast, Rattapoom. (2020). Real-Time License Plate Detection for Non-Helmeted Motorcyclist Using YOLO. ICT Express. 7. 10.1016/j.icte.2020.07.008.
Danian Zheng, Yannan Zhao, Jiaxin Wang, “An efficient method of license plate location, Pattern Recognition Letters” , Volume 26, Issue 15, 2005, Pages 2431-2438, ISSN 0167-8655, https://doi.org/10.1016/j.patrec.2005.04.014.
Siebert FW, Lin H. Detecting motorcycle helmet use with deep learning. Accid Anal Prev. 2020 Jan;134:105319. doi: 10.1016/j.aap.2019.105319. Epub 2019 Nov 6. PMID: 31706186.
MeghalDarji, Jaivik Dave, K. Upla “Licence Plate Identification and Recognition for Non-Helmeted Motorcyclists using Light-weight Convolution Neural Network“ Published 2020,Computer Science 2020 International Conference for Emerging Technology (INCET).
H. Lin, J. D. Deng, D. Albers and F. W. Siebert, "Helmet Use Detection of Tracked Motorcycles Using CNN-Based Multi-Task Learning," in IEEE Access, vol. 8, pp. 162073-162084, 2020, doi: 10.1109/ACCESS.2020.3021357.
Yange Li, Han Wei, Zheng Han, et al. “Deeep Learning-Based Safety Helmet Detection in Engineering Management Based on Convolutional Neural Networks”, Volume 2020 |Article ID 9703560 | https://doi.org/10.1155/2020/9703560.
Y. Kulkarni, S. Bodkhe, A. Kamthe and A. Patil, "Automatic number plate recognition for motorcyclists riding without helmet," 2018 International Conference on Current Trends towardsConverging Technologies (ICCTCT), 2018, pp. 1-6, doi: 10.1109/ICCTCT.2018.8551001.
Wei Jia, Shiquan Xu, Yeon-Ju Yu,” Real‐time automatic helmet detection of motorcyclists in urban traffic using improved YOLOv5 detector”, IET Image Processing,2021
Jie Li and Huanming Liu and Tianzheng Wang, et al. “Safety helmet wearing detection based on image processing and machine learning” 2017 Ninth International Conference on Advanced Computational Intelligence (ICACI)}
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