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HELMETTRACK AI: CNN-BASED MULTI TASKING HELMET DETECTION ON MOVING MOTORCYCLE
Helmet Track AI is an advanced deep learningbased system designed to enhance road safety by automatically detecting helmet usage among motorcyclists in real-time traffic environments. The rapid increase in two-wheeler usage has significantly contributed to road accidents, particularly due to non-compliance with helmet regulations. To address this issue, the proposed system employs Convolutional Neural Networks (CNNs) integrated with Multi-Task Learning (MTL) to simultaneously perform motorcyclist detection and helmet classification. Unlike traditional systems that process detection and classification separately, this unified approach reduces computational redundancy and improves inference speed. The system is trained on diverse real-world datasets, enabling it to perform effectively under varying lighting conditions, weather scenarios, camera angles, and motion blur. It utilizes real-time video streams captured through surveillance cameras and processes them using advanced computer vision techniques for accurate detection. The integration of YOLO-based object detection further enhances performance by enabling fast and efficient localization of riders and helmets. Experimental results indicate that HelmetTrack AI achieves high precision, recall, and accuracy compared to conventional models. Additionally, the system supports edge deployment, making it suitable for smart city applications and automated traffic law enforcement. By providing real-time monitoring, violation detection, and data storage capabilities, the system contributes to improved compliance and reduced accident rates. Overall, HelmetTrack AI offers a scalable, efficient, and intelligent solution for modern traffic surveillance systems.
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