Automatic Detection of Helmets on Motorcyclists Using Faster - RCNN

https://doi.org/10.22146/ijccs.68245

Aliyyah Nur Azhari(1*), Wahyono Wahyono(2)

(1) Master Program of Computer Science, FMIPA UGM, Yogyakarta
(2) Department of Computer Science and Electronics, FMIPA UGM, Yogyakarta
(*) Corresponding Author

Abstract


Motorcycles have been a popular choice for a go-to daily means of transportation due to its lower price, making it affordable for high to low-class citizens. Helmets are required for every motorcycle owner so that the rider’s head is protected from accidents. However, not many people follow the rules and tend to not wear helmets and plenty of them underestimate the usage of helmets. For this, it is necessary to implement a system that can detect which rider wears the helmet or not by applying deep learning techniques. This paper aims to implement one of the deep learning techniques, which is Faster R – CNN to detect the helmets and the motorcyclists. After training 400 images using different learning rates, the mean average precision (mAP) achieved the highest with 87% using the learning rate of 0.0001


Keywords


Helmets, Motorcycles; Object detection; Deep Learning; Faster R - CNN

Full Text:

PDF


References

[1] M. Zargar, A. Khaji, and M. Karbakhsh, “Pattern of motorcycle-related injuries in Tehran, 1999 to 2000: a study in 6 hospitals,” Eastern Mediterranean Health Journal = La Revue De Sante De La Mediterranee Orientale = Al-Majallah Al-Sihhiyah Li-Sharq Al-Mutawassit, vol. 12, no. 1–2, pp. 81–87, Jan. 2006.

[2] Constitution Law of Republic of Indonesia (UU No. 22 Year 2009) Article 291 Paragraph 1 and 2.

[3] World Health Organization, (2006). Helmets: A Road Safety Manual for Decision-makers and Practitioners. Available at: https://apps.who.int/iris/bitstream/handle/10665/43261/9241562994_eng.pdf

[4] Y. Kulkarni, S. Bodkhe, A. Kamthe, and A. Patil, “Automatic number plate recognition for motorcyclists riding without helmet,” 2018 International Conference on Current Trends towards Converging Technologies (ICCTCT), Mar. 2018, doi: 10.1109/icctct.2018.8551001.

[5] N. Boonsirisumpun, W. Puarungroj, and P. Wairotchanaphuttha, “Automatic Detector for Bikers with no Helmet using Deep Learning,” 2018 22nd International Computer Science and Engineering Conference (ICSEC), Nov. 2018, doi: 10.1109/icsec.2018.8712778.

[6] B. Yogameena, K. Menaka, and S. Saravana Perumaal, “Deep learning-based helmet wear analysis of a motorcycle rider for intelligent surveillance system,” IET Intelligent Transport Systems, vol. 13, no. 7, pp. 1190–1198, Jul. 2019, doi: 10.1049/iet-its.2018.5241.

[7] S. Ren, K. He, R. Girshick, and J. Sun, “Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 39, no. 6, pp. 1137–1149, Jun. 2015, doi: 10.1109/tpami.2016.2577031.

[8] K. Simonyan, A. Zisserman, “Very Deep Convolutional Networks for Large-Scale Image Recognition”, arXiv:1409.1556v6, 10 Apr 2015, Available Online: https://arxiv.org/abs/1409.1556v6

[9] A.N. Azhari, “Automatic Detection of Helmets on Motorcyclists Using Faster Region Based Convolutional Neural Networks (Faster R – CNN)”, Undergraduate Thesis, Universitas Gadjah Mada, 2021.

[10] C. Vishnu; Dinesh Singh; C. Krishna Mohan; S. Babu, “Detection of motorcyclists without helmet in videos using convolutional neural network”, 2017 International Joint Conference on Neural Networks (IJCNN), 14-19 May 2017, doi: 10.1109/IJCNN.2017.7966233

[11] J. Hosang, R. Benenson, B. Schiele, “Learning non-maximum suppression”, arXiv:1705.02950v2, 9 May 2017 Available: https://arxiv.org/abs/1705.02950.



DOI: https://doi.org/10.22146/ijccs.68245

Article Metrics

Abstract views : 257 | views : 232

Refbacks

  • There are currently no refbacks.




Copyright (c) 2022 IJCCS (Indonesian Journal of Computing and Cybernetics Systems)

Creative Commons License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.



Copyright of :
IJCCS (Indonesian Journal of Computing and Cybernetics Systems)
ISSN 1978-1520 (print); ISSN 2460-7258 (online)
is a scientific journal the results of Computing
and Cybernetics Systems
A publication of IndoCEISS.
Gedung S1 Ruang 416 FMIPA UGM, Sekip Utara, Yogyakarta 55281
Fax: +62274 555133
email:ijccs.mipa@ugm.ac.id | http://jurnal.ugm.ac.id/ijccs



View My Stats1
View My Stats2