@inproceedings{4bed47dc895a49d58e57964f40cda4e9,
title = "Motorcycle Helmet Detection Benchmarking",
abstract = "In this paper, we focus on evaluating the robustness of helmet detection in the context of traffic surveillance, achieved through state-of-the-art deep learning models. This aims to contribute significantly to motorcycle safety by implementing intelligent systems adept at accurately identifying helmets. An integral component of this inquiry entails a meticulous benchmark of cutting-edge object detection models and the integration of advanced techniques, aiming not only to bolster accuracy but also to improve the overall practicality and effectiveness of helmet detection systems. The experimental results highlight the effectiveness of the state-of-the-art object detection methods in detecting helmets and the potential of transferring from the traffic domain to the construction site domain.",
author = "Kunal Agrawal and Patel, \{Vatsa S.\} and Ian Cannon and Tran, \{Minh Triet\} and Nguyen, \{Tam V.\}",
note = "Publisher Copyright: {\textcopyright} The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.; 13th International Symposium on Information and Communication Technology, SOICT 2024 ; Conference date: 13-12-2024 Through 15-12-2024",
year = "2025",
doi = "10.1007/978-981-96-4282-3\_17",
language = "English (US)",
isbn = "9789819642816",
series = "Communications in Computer and Information Science",
publisher = "Springer Science and Business Media Deutschland GmbH",
pages = "203--215",
editor = "Wray Buntine and Morten Fjeld and Truyen Tran and Minh-Triet Tran and \{Huynh Thi Thanh\}, Binh and Takumi Miyoshi",
booktitle = "Information and Communication Technology - 13th International Symposium, SOICT 2024, Proceedings",
address = "Germany",
}