An Enhanced Approach for Detecting Helmet on Motorcyclists Using Image Processing and Machine Learning Techniques

In traffic accidents, motorcycle accidents are the main cause of casualties, especially in developing countries. The main cause of fatal injuries in motorcycle accidents is that motorcycle riders or passengers do not wear helmets. In this paper, an automatic helmet detection of motorcyclists method based on deep learning is presented. The method consists of two steps. The first step uses the improved YOLOv5 detector to detect motorcycles (including motorcyclists) from video surveillance. The second step takes the motorcycles detected in the previous step as input and continues to use the improved YOLOv5 detector to detect whether the motorcyclists wear helmets. The improvement of the YOLOv5 detector includes the fusion of triplet attention and the use of soft-NMS instead of NMS. A new motorcycle helmet dataset (HFUT-MH) is being proposed, which is larger and more comprehensive than the existing dataset derived from multiple traffic monitoring in Chinese cities. Finally, the proposed method is verified by experiments and compared with other state-of-the-art methods. Our method achieves mAP of 97.7%, F1-score of 92.7% and frames per second (FPS) of 63, which outperforms other state-of-the-art detection methods.This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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“…Traditional methods [19][20][21][22][23][24][25][26] mostly adopt similar ideas as shown in Figure 1. The first step is moving object detection.…”

Section: Helmet Detection Based On Traditional Methodsmentioning

confidence: 99%

Real‐time automatic helmet detection of motorcyclists in urban traffic using improved YOLOv5 detector

Jia

Liang

et al. 2021

IET Image Processing

117

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“…The HOG descriptor combined with the Circular Hough Transform (CHT) is also used for helmet detection in [14], since geometric information obtained only from CHT may be ambiguous w.r.t heads shapes. Recently, exploiting the arc circularity detected in ROI areas with a combination with HOG features, Talaulikar et al [23] detect helmets on motorcycle riders, applying Principal Component Analysis (PCA) for performance and accuracy improvements, but they provide very little details on the statistical significance of their results and the applicability to surveillance cameras given their reliance on background removal. Meanwhile Baris and Bastanlar [24] combine an omnidirectional and a Pan-Tilt-Zoom (PTZ) camera in a hybrid camera system to detect and track traffic s cene o bjects i ncluding p edestrians, motorcycles, cars and vans.…”

Section: A Methods Based On Appearancementioning

confidence: 99%

Detection of Motorcycles in Urban Traffic Using Video Analysis: A Review

Espinosa

Velastín

Branch

2021

IEEE Trans. Intell. Transport. Syst.

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Motorcycles are Vulnerable Road Users (VRU) and as such, in addition to bicycles and pedestrians, they are the traffic actors most affected by accidents in urban areas. Automatic video processing for urban surveillance cameras has the potential to effectively detect and track these road users. The present review focuses on algorithms used for detection and tracking of motorcycles, using the surveillance infrastructure provided by CCTV cameras. Given the importance of results achieved by Deep Learning theory in the field of computer vision, the use of such techniques for detection and tracking of motorcycles is also reviewed. The paper ends by describing the performance measures generally used, publicly available datasets (introducing the Urban Motorbike Dataset (UMD) with quantitative evaluation results for different detectors), discussing the challenges ahead and presenting a set of conclusions with proposed future work in this evolving area.

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“…The head images taken were cropped manually. Talaulikar et al also used arc circularity, average intensity and color, and HOG [17].…”

Section: Helmetless Head Detectionmentioning

confidence: 99%

“…The final step is determining 3 binary string bits to compare every pair of cells on each block using Eq. (17).…”

Section: Ldb Descriptormentioning

confidence: 99%

Improving Detection Performance of Helmetless Motorcyclists Using the Combination of HOG, HOP, and LDB Descriptors

2022

IJIES

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A significant number of motorcyclists that do not wear helmets lose their lives during a traffic accident, one of the major causes of death globally. This led to the design and development of a system capable of detecting helmetless motorcyclists in real-time to reduce the number of deaths. Generally, this system consists of 3 subsystems, namely moving object segmentation, motorcycle classification, and helmetless head detection. The Histograms of Oriented Gradients (HOG) descriptor has been used in preliminary studies, which resulted in fast computation time and high accuracy. However, this descriptor was less effective when applied to images with varying lighting and was unable to distinguish local pattern features. Therefore, this study proposed a new descriptor algorithm, namely Histogram of Oriented Phase and Gradient-Local Difference Binary (HOPG-LDB), which combined the HOG, Histogram of Oriented Phase (HOP), and Local Difference Binary (LDB) descriptors. The HOP was used to enhance the inability of the HOG to be effectively used in detecting images with varying lighting, and the LDB was used to extract local pattern features. The results showed that the proposed method can improve the accuracy of motorcycle classification and helmetless head detection compared to HOG, HOP, LDB, HOG-HOP, HOG-LDB, and HOP-LDB descriptors. Furthermore, the motorcycle classification accuracies of the proposed method were 97.05%, 97.25%, and 99.35% for the JSC1, JSC2, and database1 datasets. Meanwhile, the helmetless head detection results of the proposed method were 71.21%, 66.63%, and 91.73 for the JSC1, JSC2, and database2 datasets.

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An Enhanced Approach for Detecting Helmet on Motorcyclists Using Image Processing and Machine Learning Techniques

Cited by 17 publications

References 17 publications

Real‐time automatic helmet detection of motorcyclists in urban traffic using improved YOLOv5 detector

Real‐time automatic helmet detection of motorcyclists in urban traffic using improved YOLOv5 detector

Detection of Motorcycles in Urban Traffic Using Video Analysis: A Review

Improving Detection Performance of Helmetless Motorcyclists Using the Combination of HOG, HOP, and LDB Descriptors

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