TINY ARCHITECTURE IMPLEMENTATION FOR ADAS TO PREVENT PEDESTRIAN ACCIDENT USING DEEP LEARNING

Pedestrian identification is a fundamental and critical undertaking keen video observation framework, as it gives data to semantic comprehension of the video recordings. It has an undeniable expansion to auto applications because of the potential for further developing wellbeing frameworks. In 2017, many car manufacturers offer this as an ADAS option. It is used in many cars and vehicles for automatic driver assistance systems. But when the ADAS systems detects pedestrians and fails to apply ABS on time accidents might happen. To overcome this situation our project provides an audio alert to the human passengers or driver in the vehicle so that they can apply manual brakes. In this project we will require Python libraries such as Pytorch and OpenCV. Pytorch is a broadly utilized AI library. It is popular for the YOLO (You Only Look Once) algorithm which is built for Object Detection, we are using the YOLO algorithm and customize it to detect objects on a pedestrian dataset. Python has a library pyttsx3, that is fit to switch text-over completely to audio. It extracts the label from the detected pedestrians in the video and converts the text label into speech. The algorithm detects the pedestrians when they are in a close approach to the vehicles and sends the identifies labels to the pyttsx3 speaker engine. An audio alert is generated by the engine and alerts the passengers or drivers.