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Helmet Detection
AI-powered detection to ensure rider compliance with helmet-wearing protocols.
They envisioned a Driver Safety Application featuring AI-powered mobile apps and IoT-based safety devices to monitor helmet usage, path adherence, and provide real-time safety insights.
Collaborating closely with the client, we developed a comprehensive driver safety app that integrates object detection, road identification, and real-time hazard alerts.
Built on a robust tech stack, including Android, Python, TensorFlow, and TFLite, the system ensures riders remain compliant with safety protocols while offering companies a reliable safety compliance tool. The app not only enhances safety but also supports tech modernisation in fleet management and regulatory compliance.
By combining affordability with high-quality development, the Driver Safety App offers a cost-effective solution for enhancing rider safety and ensuring regulatory compliance, while enabling our client to stay at the forefront of tech modernisation in the transportation industry.
Ensuring accurate detection of helmets in various environments and lighting conditions.
Implemented AI-based helmet detection that adapts to diverse environments using TensorFlow and TFlite.
Seamlessly integrating object detection and road/path identification into a mobile platform for real-time safety updates.
Built a robust object detection system that recognizes road and path deviations, ensuring riders remain on the correct course.
Developing IoT-based safety devices that interact with mobile applications to monitor and alert riders without causing distractions.
Developed and integrated IoT-based safety devices that monitor the rider’s surroundings and interact with the mobile application for driver safety in real time.
In remote or high-speed transit areas, stable internet connectivity is a major challenge. Continuous data transmission to the cloud can be delayed or disrupted, affecting the performance of safety features.
Implemented edge computing in the IoT devices, allowing the system to process and analyze critical data locally on the device. This ensures that essential features function effectively even without a constant internet connection
Enhanced rider safety through improved helmet detection and adherence to safety protocols.
Reduced accidents by providing real-time alerts on proximity to other vehicles and potential hazards.
Increased rider confidence and safety compliance with an easy-to-use mobile application and IoT-based safety devices.
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