A robust ensemble framework for helmet usage classification in real-world scenarios

Paper Details

Authors: Lam T.-D.; Le T.

DOI: 10.22144/ctujoisd.2026.010

Journal: CTU Journal of Innovation and Sustainable Development

Year: 2026

Publisher: Can Tho University

Document Type: Article

Open Access: All Open Access; Gold Open Access; Green Open Access

Cited by: 0

Abstract

The application of machine learning models in the analysis of helmet-related images has yielded remarkable results in identifying and classifying helmet-wearing behaviours. Previous research has employed several pretrained models to predict proper or improper helmet use, achieving high accuracy on the Helmet Wearing Image Dataset (2024), a newly introduced dataset designed to enhance classification capabilities. This study aims to improve prediction performance on helmet datasets by leveraging state-of-the-art deep learning models and ensemble techniques. Using ResNet-50, MobileNetV2, and EfficientNet-B0 models, the proposed EnsemHelmet Framework uses a soft voting ensemble to optimise the classification results, achieving an outstanding accuracy of 99.24% on the experimental dataset. The results demonstrate the potential of ensemble learning to achieve high performance. This study not only improves the accuracy of the helmet-wearing recognition system but also highlights the effectiveness of ensemble techniques in optimizing performance on real-world datasets. © 2026, Can Tho University. All rights reserved.

Keywords

Deep learning; ensemble learning; helmet usage classification; image classification

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