Academic staff

PhD - Tiny and Small Object Detection

The importance of Tiny and Small Object Detection lies in its relevance to a wide range of application fields in computer vision. This technology plays a critical role in automatic image recognition and has applications in areas such as autonomous driving, surveillance systems, robotics, and medical imaging. Small and tiny objects are often critical, as they can be easily overlooked and still provide important information. Examples of their application areas include traffic sign and pedestrian detection in autonomous driving vehicles, microorganism identification in medical images, and security threat detection in surveillance videos. Therefore, the development of efficient methods for Tiny and Small Object Detection is of great importance to improve the security and performance of computer vision systems in the real world. Therefore, it is obvious in the research work to investigate and develop deep learning methods in terms of their suitability for Tiny and Small Object Detection.

FlowPro

Das Projekt „FlowPro“, das die Mikrologistik der Zukunft einleitet, wird im Rahmen der Förderrichtlinie Modernitätsfonds („mFUND“) mit rund 2,2 Millionen EURO durch das Bundesministerium für Digitales und Verkehr gefördert. Ab dem 1. Juli 2020 werden für einen Zeitraum von drei Jahren sechs bundesweite Projektpartner die Vernetzung, Fusion und Nutzung von Mobilitäts-, Verkehrs- und Logistikdaten untersuchen und erproben.

Analysis of Object Detection Datasets for Machine Learning with Small and Tiny Objects

Lars Fichtel, Dominik Erbacher, Christian Bachmeir

Eleventh International Conference on Engineering Computational Technology 2022

Tree Localization and Monitoring on Autonomous Drones Employing Deep Learning

Lars Fichtel, Alexander M. Fruhwald, Leonhard Hoesch, Vitaliy Schreibmann, Christian Bachmeir, Frank Bohlander

Proceedings of the 29th conference of FRUCT association

Towards a comprehensive attack framework against commercial and private UAV

Leonhard Hösch, Max Arndt, Lars Fichtel, Alexander M. Frühwald, Vitaliy Schreibmann, Helena Schmiedl, Andreas Schütz, Christian Bachmeir

Proceedings of the International Conference on Applied Informatics Imagination, Creativity, Design, Development - ICDD 2020

 

Towards an Industrial Recommendation System for Quality Improvement: Comparison of Python and C++ Implementations in an Edge- and Cloud Computing Environment

Alexander M. Frühwald, Steffen Kastner, Anna-Maria Schmitt, Simon Haas, Leonhard Hösch, Lars Fichtel, Christian Bachmeir

Proceedings of the International Conference on Applied Informatics Imagination, Creativity, Design, Development - ICDD 2020

• Center Artificial Intelligence and Robotics (CAIRO)
• University of Würzburg - Computer Vision Laboratory
• BayWISS-Kolleg Digitalisation