Real-Time, Multi-Object Detection for a Driverless Vehicle for a UScompany

Challenges A US-based company needed to identify in real time road hazards such as vehicles, pedestrians, strollers, traffic lights, and traffic signs.Identified objects needed to be fed to another system where a driverless vehicle could take action.To ensure accurate detection and classification, the system needed to be trained for multiple iterations and fine-tuned. Xpertnest’s Solutions Xpertnest trained deep learning models to detect and classify different objects.Used YOLO V2 architecture, Darknet19, and custom Network in Network (NiN) architecture for object detection and classification.Integrated a lane detection algorithm into the system. Value Delivered The YOLO (You Only Look Once) architecture enabled the algorithm to scan a...