Automatic Helmet Violation Detection of Motorcyclists from Surveillance Videos using Deep Learning Approaches of Computer Vision

Afzal, A. L.; Khan, Muhammad Zeeshan; Khan, Muhammad Usman Ghani

doi:10.1109/icai52203.2021.9445206

Cited by 16 publications

(5 citation statements)

References 11 publications

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“…Wei Jia and his colleagues (2021) [13] have presented a method that consists two steps, first they used Yolov5 object detection algorithms to detect the motorcyles, second step takes the output of first step whose task is to identify the motorcyclists who wear helmets, using the sequential combination of models helped them achieve mAP of 97.7%. Adil Afzal and team (2021) [14] have collected custom images of traffic in Lahore, first they have identified regions of interest using RPN and then the results are used to train faster R-CNN model, they have achieved 97.26% accuracy in detecting bikeriders with and without helmets. P. Sridhar and colleagues (2022) [15] have proposed a model which is mainly trained with Yolov2, they have used their custom dataset in order to obtain exceptional results and their model achieve good accuracy at detecting motorbike riders with and without helmet.…”

Section: Literature Reviewmentioning

confidence: 99%

Automated Helmet Monitoring System Using Deep Learning

Kavuri.K.S.V.A.Satheesh,

Nandam Sai Akhila,

Dondapati Amarnadh

et al. 2024

EPRA

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Safety and compliance are the uppermost and fundamental concerns in the modern transport subsystems. As a result, the project is essentially designed to come up with an advanced solution by combining YOLOv8 for precise identification of objects and, on the other hand, Easy OCR for reading characters. The key goals are to detect helmets, those without helmets, and identify number plates of the respective motor vehicle. With YOLOv8, we start training the model to identify not only helmets but the lack of helmets, which is necessary for compliance monitoring based on the law. Further, YOLOv8 is also designed to determine the Regions of Interest . Regarding vehicles, the model focuses mainly on license plates which are key objects. After finding the appropriate areas, Easy OCR is designed for applying optical character recognition, helping to obtain vehicle numbers of any type in the most organized, quick way. Therefore, combining YOLOv8 at the stage of object detection and Easy OCR for the recognition of characters creates a novel but, at the same time susceptible system for a vehicle control company. This integrated system is a sophisticated device for monitoring helmeted and un helmeted riders, promoting a safe and stable journey gadget. By leveraging real-time records, our answers provide precious insights into protection compliance. In summary, the aggregate of YOLOv8 and Easy OCR presents a effective answer for item popularity and conduct reputation, so that our system contributes to the development of secure and green urban mobility by means of preserving rider protection and safety. s. Index Terms - Helmet, Deep Learning, Object Detection, Character Recognition, ROI

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Section: Literature Reviewmentioning

confidence: 99%

Automated Helmet Monitoring System Using Deep Learning

Kavuri.K.S.V.A.Satheesh,

Nandam Sai Akhila,

Dondapati Amarnadh

et al. 2024

EPRA

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“…e experimental result gives an accuracy of 92.87%. Afzal et al [41] used Faster R-CNN to detect bikers that have not used helmets.…”

Section: Literature Reviewmentioning

confidence: 99%

“…A dataset of bikers with and without helmet is required to develop a system. For data acquisition, three sources include two datasets from existing works [41,46] and one dataset of self-captured data to accommodate most of the motorcycles running in different countries.…”

Section: Data Acquisitionmentioning

confidence: 99%

CNN-Based Automatic Helmet Violation Detection of Motorcyclists for an Intelligent Transportation System

Waris

Asif

Ahmad

et al. 2022

Mathematical Problems in Engineering

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An intelligent transportation system (ITS) is an advanced application that supports multiple transport and traffic management modes. ITS services include calling for emergency rescue and monitoring traffic laws with the help of roadside units. It is observed that many people lose their lives in motorbike accidents mainly due to not wearing helmets. Automatic helmet violation detection of motorcyclists from real-time videos is a demanding application in ITS. It enables one to spot and penalize bikers without a helmet. So, there is a need to develop a system that automatically detects and captures motorbikers without a helmet in real time. This work proposes a system to detect helmet violations automatically from surveillance videos captured by roadside-mounted cameras. The proposed technique is based on faster region-based convolutional neural network (R-CNN) deep learning model that takes video as an input and performs helmet violation detection to take necessary actions against traffic rule violators. Experimental analysis shows that the proposed system gives an accuracy of 97.69% and supersedes its competitors.

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“…Goyal [22] et al addressed complex environmental and occlusion issues by introducing an Amodal regressor that effectively deals with occlusion and overlap between motorcycles in crowded scenarios, thereby reducing false positives and false negatives caused by similarities between helmets, black hair, bandanas, and hats. Afzal A [23] et al proposed a method based on Faster R-CNN for automatic detect of helmet-wearing motorcyclists riders in surveillance video. First, the candidate region proposal network was used to select the candidate boxes from the convolutional feature map for helmet detection, and finally the detected helmet is recognised.…”

Section: Introductionmentioning

confidence: 99%

Development of a RT-DETR-based model for detecting two wheelers helmet

Liu,

Yue,

et al. 2024

Preprint

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The field of helmet detection faces challenges due to small targets, complex backgrounds, and confusion with neighbouring objects. This paper proposes a faster real-time two-wheeler helmet detection model based on the real-time detection transformer (RT-DETR) to address these issues. FRT-DETR includes a new FasterRNet structure designed using model re-parameterisation technique to improve detection performance while meeting real-time requirements for practical applications. Secondly, the proposal introduces a mixed global attention (MGA) module to address the issue of object confusion with neighbouring objects. The module combines channel and spatial information, as well as local and global information, resulting in a significant improvement in network performance. A new module called the cross-stage partial parallel atrous convolution (CSPPAC) is proposed to increase the receptive field. This is achieved by using convolutions with different atrous rates to capture multi-scale information and enhance feature representation. Additionally, the detection performance for small-sized helmet objects is improved through the use of a new channel-gated up-sample and down-sample to strengthen meaningful features and suppress redundant and irrelevant features. The experimental results indicate that FRT-DETR enhances the mean average precision (mAP) by 2.3% and increases the inference speed to 111.3 frames per second (FPS). These improvements significantly enhances the model's capability in detecting small objects and dense scenes, meeting the real-time requirement while ensuring detection accuracy. Therefore, it provides an effective solution for real-time detection of two-wheeler helmets.

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Automatic Helmet Violation Detection of Motorcyclists from Surveillance Videos using Deep Learning Approaches of Computer Vision

Cited by 16 publications

References 11 publications

Automated Helmet Monitoring System Using Deep Learning

Automated Helmet Monitoring System Using Deep Learning

CNN-Based Automatic Helmet Violation Detection of Motorcyclists for an Intelligent Transportation System

Development of a RT-DETR-based model for detecting two wheelers helmet

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