Real-Time Obstacle Detection with YOLOv8 in a WSN Using UAV Aerial Photography
Nowadays, wireless sensor networks (WSNs) have a significant and long-lasting impact on numerous fields that affect all facets of our lives, including governmental, civil, and military applications. WSNs contain sensor nodes linked together via wireless communication links that need to relay data in...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-10-01
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| Series: | Journal of Imaging |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2313-433X/9/10/216 |
| _version_ | 1797573426041323520 |
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| author | Shakila Rahman Jahid Hasan Rony Jia Uddin Md Abdus Samad |
| author_facet | Shakila Rahman Jahid Hasan Rony Jia Uddin Md Abdus Samad |
| author_sort | Shakila Rahman |
| collection | DOAJ |
| description | Nowadays, wireless sensor networks (WSNs) have a significant and long-lasting impact on numerous fields that affect all facets of our lives, including governmental, civil, and military applications. WSNs contain sensor nodes linked together via wireless communication links that need to relay data instantly or subsequently. In this paper, we focus on unmanned aerial vehicle (UAV)-aided data collection in wireless sensor networks (WSNs), where multiple UAVs collect data from a group of sensors. The UAVs may face some static or moving obstacles (e.g., buildings, trees, static or moving vehicles) in their traveling path while collecting the data. In the proposed system, the UAV starts and ends the data collection tour at the base station, and, while collecting data, it captures images and videos using the UAV aerial camera. After processing the captured aerial images and videos, UAVs are trained using a YOLOv8-based model to detect obstacles in their traveling path. The detection results show that the proposed YOLOv8 model performs better than other baseline algorithms in different scenarios—the F1 score of YOLOv8 is 96% in 200 epochs. |
| first_indexed | 2024-03-10T21:09:27Z |
| format | Article |
| id | doaj.art-ffa7d3a2940f429eac45dce92c639e92 |
| institution | Directory Open Access Journal |
| issn | 2313-433X |
| language | English |
| last_indexed | 2024-03-10T21:09:27Z |
| publishDate | 2023-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Journal of Imaging |
| spelling | doaj.art-ffa7d3a2940f429eac45dce92c639e922023-11-19T16:56:25ZengMDPI AGJournal of Imaging2313-433X2023-10-0191021610.3390/jimaging9100216Real-Time Obstacle Detection with YOLOv8 in a WSN Using UAV Aerial PhotographyShakila Rahman0Jahid Hasan Rony1Jia Uddin2Md Abdus Samad3Department of Computer Science, American International University-Bangladesh, Dhaka 1229, BangladeshDepartment of Computer Science and Engineering, Dhaka University of Engineering & Technology, Gazipur 1700, BangladeshArtificial Intelligence and Big Data Department, Woosong University, Daejeon 34606, Republic of KoreaDepartment of Information and Communication Engineering, Yeungnam University, Gyeongsan-si 38541, Republic of KoreaNowadays, wireless sensor networks (WSNs) have a significant and long-lasting impact on numerous fields that affect all facets of our lives, including governmental, civil, and military applications. WSNs contain sensor nodes linked together via wireless communication links that need to relay data instantly or subsequently. In this paper, we focus on unmanned aerial vehicle (UAV)-aided data collection in wireless sensor networks (WSNs), where multiple UAVs collect data from a group of sensors. The UAVs may face some static or moving obstacles (e.g., buildings, trees, static or moving vehicles) in their traveling path while collecting the data. In the proposed system, the UAV starts and ends the data collection tour at the base station, and, while collecting data, it captures images and videos using the UAV aerial camera. After processing the captured aerial images and videos, UAVs are trained using a YOLOv8-based model to detect obstacles in their traveling path. The detection results show that the proposed YOLOv8 model performs better than other baseline algorithms in different scenarios—the F1 score of YOLOv8 is 96% in 200 epochs.https://www.mdpi.com/2313-433X/9/10/216YOLOv8wireless sensor networks (WSNs)obstacle detectionunmanned aerial vehicles (UAVs)UAV aerial photography |
| spellingShingle | Shakila Rahman Jahid Hasan Rony Jia Uddin Md Abdus Samad Real-Time Obstacle Detection with YOLOv8 in a WSN Using UAV Aerial Photography Journal of Imaging YOLOv8 wireless sensor networks (WSNs) obstacle detection unmanned aerial vehicles (UAVs) UAV aerial photography |
| title | Real-Time Obstacle Detection with YOLOv8 in a WSN Using UAV Aerial Photography |
| title_full | Real-Time Obstacle Detection with YOLOv8 in a WSN Using UAV Aerial Photography |
| title_fullStr | Real-Time Obstacle Detection with YOLOv8 in a WSN Using UAV Aerial Photography |
| title_full_unstemmed | Real-Time Obstacle Detection with YOLOv8 in a WSN Using UAV Aerial Photography |
| title_short | Real-Time Obstacle Detection with YOLOv8 in a WSN Using UAV Aerial Photography |
| title_sort | real time obstacle detection with yolov8 in a wsn using uav aerial photography |
| topic | YOLOv8 wireless sensor networks (WSNs) obstacle detection unmanned aerial vehicles (UAVs) UAV aerial photography |
| url | https://www.mdpi.com/2313-433X/9/10/216 |
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