Evaluating the Effectiveness of Artificial Intelligence (AI) Cameras in Detecting Helmet Use

Jetsada Kumphong

doi:10.55003/ETH.430304

Authors

Jetsada Kumphong Faculty of Technical Education, Rajamangala University of Technology Isan, Khon Kaen Campus, Thailand

DOI:

https://doi.org/10.55003/ETH.430304

Keywords:

AI, Camera, Helmet-Wearing, Motorcycle, Accidents, Traffic Safety

Abstract

Currently, Thailand has a higher death rate from motorcycle accidents than from other vehicles. Wearing a helmet can reduce the severity of head injuries in accidents, since the head is a crucial part of the human body. The objective of this research was to evaluate the effectiveness of AI camera detection of helmet-wearing behaviours (both drivers and passengers) and to find the relationship between the accuracy of AI cameras in detecting helmet-wearing behaviours and the camera installation points. Data was collected from two AI camera installation points, observing a total of 674 motorcycles. The data was analyzed using descriptive statistics and the Pearson Chi-Square test, as well as a logistic regression analysis. The study found that AI cameras could accurately detect helmet-wearing behaviours, with Point 1 having a detection accuracy of 73.7% and Point 2 having a detection accuracy of 89.5% (Point 1 was a two-lane, one-way roadway featuring a covered pedestrian walkway with high foot traffic; whereas Point 2 was a two-lane, bi-directional roadway flanked by drainage ditches on both sides with no dedicated pedestrian facilities.). Additionally, there was a significant difference in the detection accuracy of the AI cameras between the two installation points (p-value < 0.05), as well as a difference in the detection accuracy based on helmet-wearing behaviours (p-value < 0.05). In addition, analysis revealed that camera location and helmet type had significantly impacted detection accuracy. Specifically, Point 2 outperformed Point 1, and Type 2 helmet detection showed a 23-fold higher accuracy compared to other types (p-value < 0.05)

References

World Health Organization, “Section 1. The global burden of road traffic deaths,” in Global Status Report on Road Safety 2023, Geneva, Switzerland: World Health Organization (WHO), 2023, sec. 1, pp. 3–11.

World Health Organization, “Section 2. COUNTRY/AREA PROFILES,” in Global Status Report on Road Safety 2018, Geneva, Switzerland: World Health Organization (WHO), 2018, sec. 2, pp. 92–226.

A. A. Hyder, H. Waters, T. Phillips and J. Rehwinkel, “Exploring the Economics of Motorcycle Helmet Laws—Implications for Low and Middle-Income Countries,” Asia Pacific Journal of Public Health, vol. 19, no. 2, pp. 16–22, 2007, doi: 10.1177/10105395070190020401.

S. Jomnonkwao, D. Watthanaklang, O. Sangphong, T. Champahom, N. Laddawan, S. Uttra and V. Ratanavaraha, “A Comparison of Motorcycle Helmet Wearing Intention and Behavior Between Urban and Rural Areas,” Sustainability, vol. 12, no. 20, 2020, Art. no. 8395, doi: 10.3390/su12208395.

M. Ichikawa, W. Chadbunchachai, and E. Marui , “Effect of the helmet act for motorcyclists in Thailand,” Accident Analysis & Prevention, vol.35, no. 2, pp. 183–189, 2003, doi: 10.1016/S0001-4575(01)00102-6.

P. Tankasem, T. Satiennam, W. Satiennam, and P. Klungboonkrong, “Automated speed control on urban arterial road: An experience from Khon Kaen City, Thailand,” Transportation Research Interdisciplinary Perspectives, vol. 1, 2019, Art. no.100032, doi: 10.1016/j.trip.2019.100032.

N. Tippayanate, T. Impool, P. Sujayanont, W. Muttitanon, Y. H. Chemin, and J. Som-Ard, “Temporal Analysis of Road Traffic Accidents at Major Intersections in Khon Kaen Province, Thailand: A Time Series Investigation from 2012 to 2021,” International Journal of Geoinformatics, vol. 20, no.7, pp.43–58, 2024 doi:10.52939/ijg.v20i7.3403.

P. Jantosut, W. Satiennam, T. Satiennam, and S. Jaensirisak, “Factors associated with the red-light running behavior characteristics of motorcyclists,” IATSS Research, vol. 45, no. 2, pp. 251–257, 2021, doi: 10.1016/j.iatssr.2020.10.003.

P. Tankasem, T. Satiennam, W. Satiennam, S. Jaensirisak, and W. Rujopakarn, “Effects of automated speed control on speeding intention and behavior on mixed-traffic urban arterial roads,” IATSS Research, vol.46, no.4, pp. 492–498, 2022, doi: 10.1016/j.iatssr.2022.08.002.

N. Tippayanate, T. Impool, P. Sujayanont, W. Muttitanon, Y. H. Chemin, and J. Som-Ard, “Spatial Distribution and Cluster Analysis of Road Traffic Accidents in Khon Kaen Municipality, Thailand,” International Journal of Geoinformatics, vol. 20, no.4, pp. 43–55, 2024, doi: 10.52939/ijg.v20i4.3149.

S. Huang, Y. Zhang, X. Li, J. Qin, Q. Fan, and G. Xia, “Detection of Helmet Violations among Electric Bicycle Riders Through Multi-Network,” Transportation Research Record: Journal of the Transportation Research, vol. 2679, no. 4, pp. 583–595, 2025, doi: 10.1177/03611981241295715.

J. Kumphong, T. Satiennam, W. Leelapatra and R. Ung-arunyawee, “Development of Artificial Intelligence Processing Through CCTV Camera for Detecting Unhelmeted Motorcyclists,” in Proc. 7th Internaonal Conference. on Structure, Engineering & Environment (SEE), Pattaya, Thailand, Nov. 10–12, 2021, pp 262–267.

P. Wonghabut, J. Kumphong, T. Satiennam, R. Ung-Arunyawee and W. Leelapatra, “Automatic helmet-wearing detection for law enforcement using CCTV cameras,” IOP Conference Series: Earth and Environmental Science, vol. 143, 2018, Art. no. 012063, doi: 10.1088/1755-1315/143/1/012063.

J. Chiverton, “Helmet presence classification with motorcycle detection and tracking,” IET Intelligent Transport Systems, vol.6, no.3, pp. 259–269, 2012, doi: 10.1049/iet-its.2011.0138.

T. Waris, M. Asif, M. B. Ahmad, T. Mahmood, S. Zafar, M. Shah, and A. Ayaz, “CNN‐Based Automatic Helmet Violation Detection of Motorcyclists for an Intelligent Transportation System,” Mathematical Problems in Engineering, vol. 2020, 2022, Art. no. 8246776, doi: 10.1155/2022/8246776.

J. Mercado Reyna, H. Luna-Garcia, C. H. Espino-Salinas, J. M. Celaya-Padilla, H. Gamboa-Rosales, J. I. Galván-Tejada, C. E. Galván-Tejada, R. Solís Robles, D. Rondon, and K. O. Villalba-Condori, “Detection of Helmet Use in Motorcycle Drivers Using Convolutional Neural Network,” Applied Sciences, vol.13, no.10, 2023, Art. no. 5882, doi: 10.3390/app13105882.

F. W. Siebert and H. Lin, “Detecting motorcycle helmet use with deep learning,” Accident Analysis & Prevention, vol. 134, 2020, Art. no. 105319, doi: 10.1016/j.aap.2019.105319.

R. Waranusast, N. Bundon, V. Timtong, C. Tangnoi, and P. Pattanathaburt, “Machine vision techniques for motorcycle safety helmet detection,” in 2013 28th International Conference on Image and Vision Computing New Zealand (IVCNZ 2013), Wellington, New Zealand, Nov. 27–29, 2016, pp. 35–40, doi: 10.1109/IVCNZ.2013.6726989.

W. Jia, S. Xu, Z. Liang, Y. Zhao, H. Min, S. Li, and Y. Yu, “Real‐time automatic helmet detection of motorcyclists in urban traffic using improved YOLOv5 detector,” IET Image Processing, vol. 15, no. 14, pp. 3623–3637, 2021, doi: 10.1049/ipr2.12295.

J. Kumphong and N. Chawapattanayotha, “Developing A CCTV-AI System with The Capability to Accurately Detect and Recognize Individuals Who Are Wearing Helmets, Specifically Targeting Riders and Passengers,” Industrial Technology Journal Surindra Rajabhat University, vol. 9, no. 2, pp. 210–224, 2024, doi: 10.14456/journalindus.2024.35.

A. H. Rubaiyat, T. T. Toma, M. Kalantari-Khandani, S. A. Rahman, L. Chen, and Y. Ye, “Automatic Detection of Helmet Uses for Construction Safety,” in 2016 IEEE/WIC/ACM International Conference on Web Intelligence Workshops (WIW), Omaha, NE, USA, Oct. 13–16, 2016, pp. 135–142, doi: 10.1109/WIW.2016.045.

Y. Li, H. Wei, Z. Han, J. Huang, and W. Wang, “Deep Learning‐Based Safety Helmet Detection in Engineering Management Based on Convolutional Neural Networks,” Advances in Civil Engineering, vol. 2020, no. 1, 2020. Art. no. 9703560, doi: 10.1155/2020/9703560.

A. Hayat and F. Morgado-Dias, “Deep Learning-Based Automatic Safety Helmet Detection System for Construction Safety,” Applied Sciences, vol. 12, no. 16, 2022, Art. no. 8268, doi: 10.3390/app12168268.

N. Singhata, “The 2D Barcodes Identify the Workpieces by using Microcontroller Interface between Image Processing and PLC Machine,” Walailak Journal of Science and Technology (WJST), vol. 18, no. 18, 2021, Art. no. 9539, doi: 10.48048/wjst.2021.9539.

X. Yang, J. Wang, and M. Dong, “SDCB-YOLO: A High-Precision Model for Detecting Safety Helmets and Reflective Clothing in Complex Environments,” Applied Sciences, vol. 14, no. 16, 2024, Art. no. 7267, doi: 10.3390/app14167267.

Q. B. Vo, T. H. T. Nguyen, T. H. T. Hoang, D. T. Tran, and H. B. Ly, “Enhancing construction safety management efficiency with AI-Powered real-time helmet detection,” Journal of Science and Transport Technology, vol. 5, no. 1, pp. 77–91, 2025, doi: 10.58845/jstt.utt.2025.en.5.1.77-91.

J. Han, Z. Li, G. Cui, and J. Zhao, “EGS-YOLO: A Fast and Reliable Safety Helmet Detection Method Modified Based on YOLOv7,” Applied Sciences, vol. 14, no. 17, 2024, Art. no. 7923, doi: 10.3390/app14177923.

R. Kaur, J. Singh, and S. Sharma, “Enhanced Helmet Detection in Surveillance Systems with YOLOv6 for Accident Prevention and Safety Compliance,” Journal of Scientific & Industrial Research (JSIR), vol. 84, no. 5, pp. 601–613, 2025, doi: 10.56042/jsir.v84i5.15782.

C. M. V. Wong, Y. N. Yum, and R. Y. -Y. Chan, “AI-Driven Learning Analytics for Applied Behavior Analysis Therapy,” IEEE Transactions on Learning Technologies, vol. 18, pp. 1097–1111, 2025, doi: 10.1109/TLT.2025.3637864.

J. Sunkpho and W. Wipulanusat, “The Role of Data Visualization and Analytics of Highway Accidents,” Walailak Journal of Science and Technology (WJST), vol. 17, no. 12, pp. 1379–1389, 2020, doi: 10.48048/wjst.2020.10739.