Automatic Motorcyclist Helmet Rule Violation Detection using Tensorflow &amp; Keras in OpenCV

Soni, Akanksha; Singh, Arun

doi:10.1109/sceecs48394.2020.55

Cited by 17 publications

(2 citation statements)

References 5 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Later approaches, like Vishnu et al [16] in 2017, employed adaptive background subtraction on video frames to identify moving objects, mainly motorcyclists. Then, they identified individuals who were not wearing helmets by using Convolutional Neural Networks (CNNs) to separate motorcyclists from the detected moving objects.…”

Section: B Related Workmentioning

confidence: 99%

LoLTV: A Low Light Two-Wheeler Violation Dataset With Anomaly Detection Technique

Bose,

Kolekar,

Nawale

et al. 2023

IEEE Access

View full text Add to dashboard Cite

Detecting traffic violations is essential for improving road safety, ensuring rule compliance, and maintaining smooth traffic flow. It also aids in holding violators accountable and supports data-driven decision-making for infrastructure enhancements. To address these challenges, the integration of AI-based methods for automated violation detection is increasingly vital, reducing the need for manual oversight. Lowlight conditions pose additional difficulties, as violations become harder to detect. In this study, we created a novel dataset containing 1032 images with 1475 two-wheeler violations under low-light conditions. We propose a real-time deep learning system using YOLO-v8 for two-wheeler violation detection. Our system addresses the challenge of low-light conditions by incorporating a real-time low-light video enhancement module. Through comprehensive evaluations, our system has achieved an average precision of 98.2%, recall of 97.5%, and an accuracy of 97.05% when tested on our custom dataset. Notably, it successfully detected 172 out of 188 violations in the test dataset and exhibited 60% faster processing compared to other stateof-the-art methods. This suggests that our system not only outperforms existing methods on public datasets but also excels in terms of performance and accuracy when applied to the specifically constructed low-light traffic dataset. Furthermore, our system's practical scalability is evident through its integration with multiple devices and CCTV systems.

show abstract

Section: B Related Workmentioning

confidence: 99%

LoLTV: A Low Light Two-Wheeler Violation Dataset With Anomaly Detection Technique

Bose,

Kolekar,

Nawale

et al. 2023

IEEE Access

View full text Add to dashboard Cite

show abstract

“…In spite of being the moment most crowded nation within the world, India has the world's tenth greatest economy [1].In a thickly populated country like India, motorbikes are the foremost conservative and helpful mode of portability. Head protectors are implied to secure the head, which could be a exceedingly defenseless parcel of our body, and they may be fundamental for all cruiser riders.…”

Section: Introductionmentioning

confidence: 99%

Helmet Rule Violation Detection Using Deep Learning

2024

IJSREM

View full text Add to dashboard Cite

The widespread non-compliance of helmet-wearing regulations among motorcyclists in India poses a significant threat to road safety. This paper introduces a new approach to address this issue through the developing a Helmet Rule Violation Detection system. Given the challenges faced by traffic police in enforcing helmet regulations, the proposedsystem leverages computer vision techniques to automatically identify motorcyclists without helmets. The methodology involves the detection of moving vehicles through thresholding, followed by a classification process based on area and aspect ratio to distinguish between motorcyclists and non-motorcyclists. The subsequent identification of helmet violation is achieved by isolating relevant blobs and employing a classification mechanism. By automating this process, the system aims to reduce the burden on traffic police and enhance the overall safety of road users. The proposed approach not only contributes to the mitigation of helmet rule violations but also promotes responsible road behavior, fostering a safer and more compliant traffic environment in India.

show abstract