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Real-time Multi-Class Helmet Violation Detection Using Few-Shot Data Sampling Technique and YOLOv8
Computer Vision and Pattern Recognition · Submitted 13 Apr 2023
abstract compiled · 2026-09-23 · no language-model review
The authors' main claim The experimental results demonstrate the effectiveness, efficiency, and robustness of the proposed system.
The compiled record
The paper's own sentences, arranged. Each label is drawn from a closed vocabulary; the number beside it is the probability the classifier gave that label. Numbers in the tables are the authors', checked by code. No sentence in this record was written by a model.
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The abstract, sentence by sentence
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.
background contribution method result interpretation main claim
| # | sentence | role | the authors' own work | states the main conclusion | strength |
|---|---|---|---|---|---|
| s0 | Traffic safety is a major global concern. | background 1.00 | 0.03 | 0.01 | asserted 1.00 |
| s1 | Helmet usage is a key factor in preventing head injuries and fatalities caused by motorcycle accidents. | background 1.00 | 0.04 | 0.02 | asserted 1.00 |
| s2 | However, helmet usage violations continue to be a significant problem. | background 0.92 | 0.05 | 0.02 | asserted 1.00 |
| s3 | To identify such violations, automatic helmet detection systems have been proposed and implemented using computer vision techniques. | background 0.99 | 0.06 | 0.02 | asserted 1.00 |
| s4 | Real-time implementation of such systems is crucial for traffic surveillance and enforcement, however, most of these systems are not real-time. | gap 0.93 | 0.10 | 0.04 | asserted 1.00 |
| s5 | This study proposes a robust real-time helmet violation detection system. | contribution 1.00 | 0.78 | 0.30 | asserted 1.00 |
| s6 | 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. | method 0.94 | 0.86 | 0.15 | asserted 1.00 |
| s7 | 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. | result 1.00 | 0.96 | 0.09 | asserted 1.00 |
| s8 | The experimental results demonstrate the effectiveness, efficiency, and robustness of the proposed system. | interpretation 0.80 | 0.90 | 0.71 | asserted 1.00 |
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The paper
Original abstract
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.
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Compares with 1 paper
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The model uses the Darknet-53 backbone network, which is faster and more precise than the previous YOLOv7 [ref] network.
YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors · Helmet Detection Models · 0.81
Cites as background 1 paper
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The YOLOv7 [ref] model had numerous changes to its architecture that distinguished it from YOLOv5.
YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors · Helmet Detection Models · 0.83
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