Experimental Validation of UAV Search and Detection System in Real Wilderness Environment
Search and rescue (SAR) missions require reliable search methods to locate survivors, especially in challenging environments. Introducing unmanned aerial vehicles (UAVs) can enhance the efficiency of SAR missions while simultaneously increasing the safety of everyone involved. Motivated by this, we...
| الحاوية / القاعدة: | Drones |
|---|---|
| المؤلفون الرئيسيون: | , , , |
| التنسيق: | مقال |
| اللغة: | الإنجليزية |
| منشور في: |
MDPI AG
2025-07-01
|
| الموضوعات: | |
| الوصول للمادة أونلاين: | https://www.mdpi.com/2504-446X/9/7/473 |
| _version_ | 1849373366482370560 |
|---|---|
| author | Stella Dumenčić Luka Lanča Karlo Jakac Stefan Ivić |
| author_facet | Stella Dumenčić Luka Lanča Karlo Jakac Stefan Ivić |
| author_sort | Stella Dumenčić |
| collection | DOAJ |
| container_title | Drones |
| description | Search and rescue (SAR) missions require reliable search methods to locate survivors, especially in challenging environments. Introducing unmanned aerial vehicles (UAVs) can enhance the efficiency of SAR missions while simultaneously increasing the safety of everyone involved. Motivated by this, we experiment with autonomous UAV search for humans in Mediterranean karst environment. The UAVs are directed using the Heat equation-driven area coverage (HEDAC) ergodic control method based on known probability density and detection function. The sensing framework consists of a probabilistic search model, motion control system, and object detection enabling to calculate the target’s detection probability. This paper focuses on the experimental validation of the proposed sensing framework. The uniform probability density, achieved by assigning suitable tasks to 78 volunteers, ensures the even probability of finding targets. The detection model is based on the You Only Look Once (YOLO) model trained on a previously collected orthophoto image database. The experimental search is carefully planned and conducted, while recording as many parameters as possible. The thorough analysis includes the motion control system, object detection, and search validation. The assessment of the detection and search performance strongly indicates that the detection model in the UAV control algorithm is aligned with real-world results. |
| format | Article |
| id | doaj-art-ea3b2c0e9a474b0a8db08f3d8d4d5708 |
| institution | Directory of Open Access Journals |
| issn | 2504-446X |
| language | English |
| publishDate | 2025-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| spelling | doaj-art-ea3b2c0e9a474b0a8db08f3d8d4d57082025-08-20T03:58:27ZengMDPI AGDrones2504-446X2025-07-019747310.3390/drones9070473Experimental Validation of UAV Search and Detection System in Real Wilderness EnvironmentStella Dumenčić0Luka Lanča1Karlo Jakac2Stefan Ivić3Faculty of Engineering, University of Rijeka, Vukovarska 58, 51000 Rijeka, CroatiaFaculty of Engineering, University of Rijeka, Vukovarska 58, 51000 Rijeka, CroatiaFaculty of Engineering, University of Rijeka, Vukovarska 58, 51000 Rijeka, CroatiaFaculty of Engineering, University of Rijeka, Vukovarska 58, 51000 Rijeka, CroatiaSearch and rescue (SAR) missions require reliable search methods to locate survivors, especially in challenging environments. Introducing unmanned aerial vehicles (UAVs) can enhance the efficiency of SAR missions while simultaneously increasing the safety of everyone involved. Motivated by this, we experiment with autonomous UAV search for humans in Mediterranean karst environment. The UAVs are directed using the Heat equation-driven area coverage (HEDAC) ergodic control method based on known probability density and detection function. The sensing framework consists of a probabilistic search model, motion control system, and object detection enabling to calculate the target’s detection probability. This paper focuses on the experimental validation of the proposed sensing framework. The uniform probability density, achieved by assigning suitable tasks to 78 volunteers, ensures the even probability of finding targets. The detection model is based on the You Only Look Once (YOLO) model trained on a previously collected orthophoto image database. The experimental search is carefully planned and conducted, while recording as many parameters as possible. The thorough analysis includes the motion control system, object detection, and search validation. The assessment of the detection and search performance strongly indicates that the detection model in the UAV control algorithm is aligned with real-world results.https://www.mdpi.com/2504-446X/9/7/473UAVsearch and rescueexperimentYOLOHEDAC |
| spellingShingle | Stella Dumenčić Luka Lanča Karlo Jakac Stefan Ivić Experimental Validation of UAV Search and Detection System in Real Wilderness Environment UAV search and rescue experiment YOLO HEDAC |
| title | Experimental Validation of UAV Search and Detection System in Real Wilderness Environment |
| title_full | Experimental Validation of UAV Search and Detection System in Real Wilderness Environment |
| title_fullStr | Experimental Validation of UAV Search and Detection System in Real Wilderness Environment |
| title_full_unstemmed | Experimental Validation of UAV Search and Detection System in Real Wilderness Environment |
| title_short | Experimental Validation of UAV Search and Detection System in Real Wilderness Environment |
| title_sort | experimental validation of uav search and detection system in real wilderness environment |
| topic | UAV search and rescue experiment YOLO HEDAC |
| url | https://www.mdpi.com/2504-446X/9/7/473 |
| work_keys_str_mv | AT stelladumencic experimentalvalidationofuavsearchanddetectionsysteminrealwildernessenvironment AT lukalanca experimentalvalidationofuavsearchanddetectionsysteminrealwildernessenvironment AT karlojakac experimentalvalidationofuavsearchanddetectionsysteminrealwildernessenvironment AT stefanivic experimentalvalidationofuavsearchanddetectionsysteminrealwildernessenvironment |