Jon Muhovič

Assistant

Research

My primary research interests include perception methods for autonomous boats, deep learning, apttern recognition and cognitive systems.

Autonomous boats perception methods

Unmnanned surface vehicles (USV) are robotic boats that can be used for coastal patrolling in a numerous applications ranging from surveillance to water cleanness control. We are developing computer vision algorithms that enable autonomous operation in the highly dynamic environments in which the USVs are applied.

Traffic-sign detection

We explore automation of traffic-sign inventory management using deep-learning models. Models such as Faster R-CNN and Mask R-CNN are improved and applied to traffic sign detection. Instead of specializing in automated detection for only several traffic sign categories we explore possibility of automating the detection of over 200 different traffic signs that are needed to automate the traffic-sign inventory management.

Drone tracking

The tracking algorithms we developed can be applied to autonomous robots like drones. Here are some results from this research application.

Projects:

DAViMaR - Adaptive deep perception methods for autonomous surface vehicles

April 2020 - August 2023
The project primary goal is to develop the next-generation maritime environment perception methods, which will harvest the power of end-to-end trainable deep models for essential challenges of safe operation like: general obstacle detection with re-identification, implicit detection of hazardous areas and sensor fusion for improved detection.

ViAMaRo - Robust computer vision methods for autonomous water surface vehicles

May 2017 - April 2020
The project primary goal is to develop functionalities required for robust autonomous navigation of USVs in uncontrolled environments, primarily relying on the captured visual information. The project focuses on obstacle detection using monocular and stereo systems, development of efficient visual tracking algorithms for marine environments and environment representation through sensor fusion.

DIVID - Detection of inconsistencies in complex visual data using deep learning

July 2018 - December 2021
The objective of the project is to develop novel deep learning methods for modelling complex consistency and detecting inconsistencies in visual data using training images annotated with different levels of accuracy. The main project goal is to go beyond the traditional supervised learning, where all anomalies on all training images have to be adequately labelled.

Teaching

Publications