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


  • 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.
Geospatial Intelligence, Data Processing Platform​, ERP Solution, Computer Vision Solution, AgriX Solution, iCare Solution, Claim Management Solution, Innovation Solution, SERVICES, Artificial Intelligence SERVICES, Augmented & Virtual Reality services, Smart City services, Data Management & Insight services, Hyper Automation services, Product Development services, Application Support Management services,

Value Delivered

  • The YOLO (You Only Look Once) architecture enabled the algorithm to scan a frame once to detect multiple objects, increasing speed.
  • Enabled safe and reliable driverless navigation.
  • The client saved time and was able to raise the quality of its candidates.

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