Type:         Applied Research

Employer:  Rose-Hulman Institute of Technology

Objective:  Object Detection, Tracking, and Analysis
                  for a 3D Mapping LiDAR

Duration:   Jan 2021 - Feb 2021

Trained convolutional neural networks in PyTorch and Tensorflow as the baseline agents to determine the imaged objects' position, velocity, classification, and orientation;

Implemented a neural architecture search system specialized for R-CNN and YOLO using the NeuroEvolution of Augmenting Topologies paradigm;

Integrated the LiDAR interface into the deep learning architecture to allow continuous data collection, preprocessing, and real-time learning.