Abstract: This paper presents an advanced approach for helmet detection at traffic signals using deep learning techniques, addressing the critical issue of motorcycle safety in urban environments. With the rising number of road accidents involving two-wheeled vehicles, effective monitoring of helmet usage is essential for enhancing compliance with safety regulations. We propose a convolutional neural network (CNN)-based model that analyses video feeds from traffic signals to detect and classify helmeted riders. Our methodology involves the collection and annotation of a diverse dataset that includes various riding scenarios, helmets, and lighting conditions to ensure robust model training. Leveraging.....
[1]
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