# Real-time Multi-Class Helmet Violation Detection Using Few-Shot Data Sampling Technique and YOLOv8

arXiv: https://arxiv.org/abs/2304.08256 · HTML: https://mlsanity.com/paper/2304.08256/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 23 references that are in the map.
- First posted on arXiv 13 Apr 2023.

## Builds on (most cited first, 6 of 6)

- [YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors](https://mlsanity.com/paper/2207.02696/) (2022)
- [SCAN: Learning to Classify Images without Labels](https://mlsanity.com/paper/2005.12320/) (2020)
- [A Vision-based System for Traffic Anomaly Detection using Deep Learning and Decision Trees](https://mlsanity.com/paper/2104.06856/) (2021)
- [Mobile Sensing for Multipurpose Applications in Transportation](https://mlsanity.com/paper/2106.10733/) (2021)
- [A Region-Based Deep Learning Approach to Automated Retail Checkout](https://mlsanity.com/paper/2204.08584/) (2022)
- [Driver Maneuver Detection and Analysis using Time Series Segmentation and Classification](https://mlsanity.com/paper/2211.06463/) (2022)

## Built on by (most cited first, 13 of 13)

- [The 7th AI City Challenge](https://mlsanity.com/paper/2304.07500/) (2023)
- [Immature Green Apple Detection and Sizing in Commercial Orchards using YOLOv8 and Shape Fitting Techniques](https://mlsanity.com/paper/2401.08629/) (2023)
- [Integrating Travel Behavior Forecasting and Generative Modeling for Predicting Future Urban Mobility and Spatial Transformations](https://mlsanity.com/paper/2503.21158/) (2025)
- [Low-Light Image Enhancement Framework for Improved Object Detection in Fisheye Lens Datasets](https://mlsanity.com/paper/2404.10078/) (2024)
- [An Improved ResNet50 Model for Predicting Pavement Condition Index (PCI) Directly from Pavement Images](https://mlsanity.com/paper/2504.18490/) (2025)
- [Towards certifiable AI in aviation: landscape, challenges, and opportunities](https://mlsanity.com/paper/2409.08666/) (2024)
- [Towards General Urban Monitoring with Vision-Language Models: A Review, Evaluation, and a Research Agenda](https://mlsanity.com/paper/2510.12400/) (2025)
- [Real-Time Helmet Violation Detection Using YOLOv5 and Ensemble Learning](https://mlsanity.com/paper/2304.09246/) (2023)
- [A Non-Uniform Low-Light Image Enhancement Method with Multi-Scale Attention Transformer and Luminance Consistency Loss](https://mlsanity.com/paper/2312.16498/) (2023)
- [DashCop: Automated E-ticket Generation for Two-Wheeler Traffic Violations Using Dashcam Videos](https://mlsanity.com/paper/2503.00428/) (2025)
- [Prompt-Guided Spatial Understanding with RGB-D Transformers for Fine-Grained Object Relation Reasoning](https://mlsanity.com/paper/2510.11996/) (2025)
- [VisionGuard: Synergistic Framework for Helmet Violation Detection](https://mlsanity.com/paper/2506.21005/) (2025)
- [Neural expressiveness for beyond importance model compression](https://mlsanity.com/paper/2512.06440/) (2025)

## Abstract (arXiv)

Traffic safety is a major global concern. Helmet usage is a key factor in preventing head injuries and fatalities caused by motorcycle accidents. However, helmet usage violations continue to be a significant problem. To identify such violations, automatic helmet detection systems have been proposed and implemented using computer vision techniques. Real-time implementation of such systems is crucial for traffic surveillance and enforcement, however, most of these systems are not real-time. This study proposes a robust real-time helmet violation detection system. The proposed system utilizes a unique data processing strategy, referred to as few-shot data sampling, to develop a robust model with fewer annotations, and a single-stage object detection model, YOLOv8 (You Only Look Once Version 8), for detecting helmet violations in real-time from video frames. Our proposed method won 7th place in the 2023 AI City Challenge, Track 5, with an mAP score of 0.5861 on experimental validation data. The experimental results demonstrate the effectiveness, efficiency, and robustness of the proposed system.