Visual Recognition to Identify Helmet on Motorcycle Rider Using Convolutional Neural Network


  • Kevin Alexander Computer Science Department, School of Computer Science, Bina Nusantara University, Jakarta, 11480, Indonesia
  • Rayhan Ardiya Dwantara Computer Science Department, School of Computer Science, Bina Nusantara University, Jakarta, 11480, Indonesia
  • Raihan Muhammad Naufal Computer Science Department, School of Computer Science, Bina Nusantara University, Jakarta, 11480, Indonesia
  • Derwin Suhartono Computer Science Department, School of Computer Science, Bina Nusantara University, Jakarta, 11480, Indonesia



Visual Recognition, Helmet, Motorcycle Rider, Convolutional Neural Network


The amount of motorcycle accidents is increasing each year. The main reason is that the riders do not wear a helmet. The research aims to minimize the accident by training the machine learning using the IBM Watson Studio. It trains the data about “wearing helmet” and “not wearing helmet”. The used method is Convolutional Neural Network (CNN). About 170 image datasets are used. CNN is conducted on the input image using a kernel or filter. The filter will multiply its values with the overlapping values of the image while also sliding and adding them all to produce a single value for each of them until the entire images have passed and finished. After CNN method is done, the researchers can classify the images by using supervised learning. It can identify whether the rider is wearing a helmet or not simply by scanning a picture on the street. The result shows high accuracy of 92.87%. The method can minimize the percentage of motorcycle accidents done by not wearing a helmet.


G. Y. N. N. Adi, M. H. Tandio, V. Ong, and D. Suhartono, “Optimization for automatic personality recognition on Twitter in Bahasa Indonesia,” Procedia Computer Science, vol. 135, pp. 473–480, 2018.

R. Kurniawan. (2019) Angka kecelakaan lalu lintas di 2019 meningkat. [Online]. Available: https://otomotif. [3] E. Alpaydin, Introduction to machine learning. Cambridge, Massachusetts: MIT Press, 2020.

A. Jahangiri, H. A. Rakha, and T. A. Dingus, “Adopting machine learning methods to predict red-light running violations,” in 2015 IEEE 18th International Conference on Intelligent Transportation Systems. Las Palmas, Spain: IEEE, Sept. 15–18, 2015, pp. 650–655.

L. J. Li, H. Su, Y. Lim, and L. Fei-Fei, “Object bank: An object-level image representation for high-level visual recognition,” International Journal of Computer Vision, vol. 107, no. 1, pp. 20–39, 2014.

C. Cusano and P. Napoletano, “Visual recognition of aircraft mechanical parts for smart maintenance,” Computers in Industry, vol. 86, no. April, pp. 26–33, 2017.

Y. Guo, M. Bennamoun, F. Sohel, M. Lu, and J. Wan, “3D object recognition in cluttered scenes with local surface features: A survey,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 36, no. 11, pp. 2270–2287, 2014.

K. Yanai and Y. Kawano, “Food image recognition using deep convolutional network with pre-training and fine-tuning,” in 2015 IEEE International Conference on Multimedia & Expo Workshops (ICMEW). Turin, Italy: IEEE, June 29–July 3, 2015, pp. 1–6.

X. Sun, J. Shi, J. Dong, and X. Wang, “Fish recognition from low-resolution underwater images,” in 2016 9th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI). Datong, China: IEEE, Oct. 15–17, 2016, pp. 471–476.

S. Sladojevic, M. Arsenovic, A. Anderla, D. Culibrk, and D. Stefanovic, “Deep Neural Networks based recognition of plant diseases by leaf image classification,” Computational Intelligence and Neuroscience, vol. 2016, pp. 1–11, 2016.

E. Aguilar, M. Bola˜nos, and P. Radeva, “Food recognition using fusion of classifiers based on CNNs,” in International Conference on Image Analysis and Processing. Catania, Italy: Springer, Sept. 11–15, 2017, pp. 213–224.

S. Gilda, “Evaluating machine learning algorithms for fake news detection,” in 2017 IEEE 15th Student Conference on Research and Devel-opment (SCOReD). Putrajaya, Malaysia: IEEE, Dec. 13–14, 2017, pp. 110–115.

MathWorks. What is deep learning? How it works, techniques & applications. [Online]. Available: eid=PSM da

M. I. Jordan and T. M. Mitchell, “Machine learning: Trends, perspectives, and prospects,”Science, vol. 349, no. 6245, pp. 255–260, 2015.

D. E. King, “Dlib-ml: A machine learning toolkit,” The Journal of Machine Learning Research, vol. 10, no. 60, pp. 1755–1758, 2009.

J. Snoek, H. Larochelle, and R. P. Adams, “Practical bayesian optimization of machine learning algorithms,” in Advances in Neural Information Processing Systems 25, Lake Tahoe, Nevada, USA, Dec. 3–6, 2012, pp. 2951–2959.

Z. Ghahramani, “Probabilistic machine learning and artificial intelligence,” Nature, vol. 521, no. 7553, pp. 452–459, 2015.

M. Feurer, A. Klein, K. Eggensperger, J. Springenberg, M. Blum, and F. Hutter, “Efficient and robust automated machine learning,” in Advances in Neural Information Processing Systems 28 (NIPS 2015), Montreal, Quebec, Canada, Dec. 7–12, 2015, pp. 2962–2970.

M. Abadi, P. Barham, J. Chen, Z. Chen, A. Davis, J. Dean, M. Devin, S. Ghemawat, G. Irving, M. Isard et al., “TensorFlow: A system for largescale machine learning,” in 12th fUSENIXg Symposium on Operating Systems Design and Implementation (fOSDIg 16), Savannah, GA, USA, Nov. 2–4, 2016, pp. 265–283.

B. A. Echeagaray-Patron, D. Miramontes-Jaramillo, and V. Kober, “Conformal parameterization and curvature analysis for 3D facial recognition,” in 2015 International Conference on Computational Science and Computational Intelligence (CSCI). Las Vegas, NV, USA: IEEE, Dec. 7–9, 2015, pp. 843–844.

A. E. Pandelea, M. Budescu, and G. Covatariu, “Image processing using Artificial Neural Networks,” Buletinul Institutului Politehnic din lasi. Sectia Constructii, Arhitectura, vol. 61, no. 4, pp. 9–21, 2015.

Y. Zhang, W. C. Lin, and Y. K. S. Chin, “A pattern-recognition approach for driving skill characterization,” IEEE Transactions on Intelligent Transportation Systems, vol. 11, no. 4, pp. 905–916, 2010.

K. Dahiya, D. Singh, and C. K. Mohan, “Automatic detection of bike-riders without helmet using surveillance videos in real-time,” in 2016 International Joint Conference on Neural Networks (IJCNN). Vancouver, BC, Canada: IEEE, July 24–29, 2016, pp. 3046–3051.

J. Mistry, A. K. Misraa, M. Agarwal, A. Vyas, V. M. Chudasama, and K. P. Upla, “An automatic detection of helmeted and non-helmeted motorcyclist with license plate extraction using Convolutional Neural Network,” in 2017 Seventh International Conference on Image Processing Theory, Tools and Applications (IPTA). Montreal, QC, Canada: IEEE, Nov. 28–Dec. 1, 2017, pp. 1–6.

C. Vishnu, D. Singh, C. K. Mohan, and S. Babu, “Detection of motorcyclists without helmet in videos using Convolutional Neural Network,” in 2017 International Joint Conference on Neural Networks (IJCNN). Anchorage, AK, USA: IEEE, May 14–19, 2017, pp. 3036–3041.

P. Doungmala and K. Klubsuwan, “Helmet wearing detection in Thailand using haar like feature and circle hough transform on image processing,” in 2016 IEEE International Conference on Computer and Information Technology (CIT). Nadi, Fiji: IEEE, Dec. 8–10, 2016, pp. 611–614.