Davor Damjanovic
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Davor Damjanovic has a total of 10 co-authors. Here are the top co-authors by number of shared publications:
- Josip Balen
Faculty of Electrical Engineering, Computer Science and Information Technology Osijek, J. J. Strossmayer University of Osijek, Osijek, Croatia
3 shared publications - Antonio Antunovic
Faculty of Electrical Engineering, Computer Science and Information Technology Osijek, Josip Juraj Strossmayer University of Osijek, Kneza Trpimira 2b, 31000 Osijek, Croatia
2 shared publications - Boris Kos
Kosmos 42, Tvrđavica 173, 31000 Osijek, Croatia
1 shared publication - Dorian Činčurak
TTTech Auto d.o.o., Osijek, Croatia
1 shared publication - Emmanuel Karlo Nyarko
Faculty of Electrical Engineering, Computer Science and Information Technology Osijek, Josip Juraj Strossmayer University of Osijek, Kneza Trpimira 2b, 31000 Osijek, Croatia
1 shared publication - Franko Hržić
Center for Artificial Intelligence and Cybersecurity, University of Rijeka, Rijeka, Croatia
1 shared publication
Latest Publications
- Open Access
- 9 February 2026
- Published by MDPI AG in Engineering Proceedings
- Vol. 125 (1) , 19
Comparative Analysis of Sensors for Fire Hazard Detection in Indoor Environments
Fire hazards in closed industrial environments pose a significant threat to workers, infrastructure and production processes. Traditional detection systems, such as smoke and heat detectors, often have limitations in their settings, including delayed response time and a tendency for false alarms due to non-fire factors such as dust, humidity and vapors. This paper researches the applicability of gas sensors as an alternative or complementary method for early fire detection. This research presents an experimental evaluation of six gas sensors integrated with a microcontroller. Tests were conducted in a controlled environment, simulating industrial conditions by monitoring the combustion of different materials, such as wood, plastic and textile. Sensor responses were analyzed at horizontal distances of 2 m and 4 m from the fire source. Results show that all sensors detected combustion byproducts, with those at 2 m exhibiting a faster response and higher concentration readings. The findings confirm that a multi-sensor approach significantly increases detection reliability and enables an earlier response compared to conventional systems.
- 9 February 2026
- Published by MDPI AG in Engineering Proceedings
- Vol. 125 (1) , 19
Open AccessComparative Analysis of Sensors for Fire Hazard Detection in Indoor Environments
Fire hazards in closed industrial environments pose a significant threat to workers, infrastructure and production processes. Traditional detection systems, such as smoke and heat detectors, often have limitations in their settings, including delayed response time and a tendency for false alarms due to non-fire factors such as dust, humidity and vapors. This paper researches the applicability of gas sensors as an alternative or complementary method for early fire detection. This research presents an experimental evaluation of six gas sensors integrated with a microcontroller. Tests were conducted in a controlled environment, simulating industrial conditions by monitoring the combustion of different materials, such as wood, plastic and textile. Sensor responses were analyzed at horizontal distances of 2 m and 4 m from the fire source. Results show that all sensors detected combustion byproducts, with those at 2 m exhibiting a faster response and higher concentration readings. The findings confirm that a multi-sensor approach significantly increases detection reliability and enables an earlier response compared to conventional systems.
- 30 July 2025
- Published by Springer Nature
- p. 133-147
Mutual-Training Pseudo-labeling Framework for Fire Segmentation
- 30 July 2025
- Published by Springer Nature
- p. 133-147
Mutual-Training Pseudo-labeling Framework for Fire Segmentation
- 14 March 2025
- Published by Springer Nature in Machine Vision and Applications
- Vol. 36 (3) , 1-21
A comprehensive survey on SLAM and machine learning approaches for indoor autonomous navigation of mobile robots
- 14 March 2025
- Published by Springer Nature in Machine Vision and Applications
- Vol. 36 (3) , 1-21
A comprehensive survey on SLAM and machine learning approaches for indoor autonomous navigation of mobile robots