Traffic-Sign Detection for Driver Assistance
YOLOv8 / Faster-RCNN vision pipeline classifying traffic speed limits, warnings, and mandatory signals in real time.
Project Overview
Trained on German/Indian traffic sign benchmarks. Processes dashcam video streams at 30+ FPS, detecting speed limits, stop signs, and pedestrian crossings under adverse weather and lighting conditions. ### Practical Student Engineering Solution Fully functional hardware demonstration prototype equipped with dedicated microcontrollers, precision sensors, actuators, and an interactive cloud/mobile telemetry dashboard. ### Key Learning Outcomes - Embedded C / MicroPython firmware architecture and sensor interfacing - IoT telemetry protocols (MQTT / HTTP / BLE / LoRaWAN) - Power regulation, hardware debugging, and PCB design principles - Comprehensive technical documentation aligned with IEEE academic standards
Modern commercial and industrial infrastructure requires automated, real-time sensing and telemetry. Conventional manual monitoring suffers from high latency, human error, and lack of predictive fault visibility for Traffic-Sign Detection for Driver Assistance.
A turnkey engineering prototype integrating high-precision sensor modules, dedicated microcontroller unit, and cloud telemetry protocols to automate measurement, trigger alert thresholds, and provide remote dashboard control.