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 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.
