Presenter: Ramez Hajj, Department of Civil and Environmental Engineering, University of Illinois

October 12, 2021

Slides: https://uofi.app.box.com/s/i9toe64wy8qf7qxxumoy6uhtzjpgz913

Video: Available upon request

Abstract

In the last few years, deep learning has, in conjunction with computer vision techniques, rapidly advanced to allow the classification of objects accurately and efficiently. With the increasing use of unmanned aerial vehicles (UAVs) and the availability of high-resolution satellite data, it is more important now than ever before to have effective algorithms for determining what objects are present. Vehicle classification and counting are important problems for intelligence applications. However, strong vehicle classification algorithms which can classify individual vehicles using satellite or UAV data have yet to be developed for use by intelligence agencies. This project seeks to develop vehicle classification and detection algorithms by leveraging the latest technologies in computer vision and deep learning. Aerial image data from labeling to validation are parameterized and monitored to feed into neural networks. The new framework lays focus on Machine Learning (ML) data pipelines, to improve the quality of production ML on the operationalization of models for accurate vehicle classification and counting. Transfer learning is used on different neural network architectures such as CenterNet, YOLOv4, and YOLOv5, which are all single stage object detection algorithms. The implemented algorithms are subjected to hyperparameter tuning to obtain the best performing classification model or combination thereof.

Biography

Ramez Hajj is an Assistant Professor in the department of Civil and Environmental Engineering. Prior to joining UIUC, he completed his Ph.D. and Master’s in Civil Engineering at the University of Texas at Austin. He obtained his Bachelor’s Degree in Civil Engineering with a minor in Engineering Science and Mechanics from Virginia Tech. Dr. Hajj’s research focuses on transportation engineering and spans everything related to asphalt materials and flexible pavements, novel construction materials, computer vision for civil engineering applications. To date, he has published in a wide range of peerreviewed journals in these areas and presented his work at many international conferences. In addition to the New Frontiers Initiative, Dr. Hajj’s research is currently funded by the Illinois Department of Transportation (IDOT), the Smart Transportation Infrastructure Initiative (STII), the Illinois-Indiana Sea Grant, and numerous industry partners.