Integrating UAVs and YOLO Deep Learning for Early-Stage Forest Fire Detection

References

1. P. Barmpoutis, P. Papaioannou, K. Dimitropoulos, N. Grammalidis, “A review on early forest fire detection systems using optical remote sensing,” Sensors, vol. 20, no. 6442, 2019.
2. R.S. Priya, K. Vani, "Deep Learning Based Forest Fire Classification and Detection in Satellite Images," 2019 11th International Conference on Advanced Computing (ICoAC), Chennai, India, 2019, pp. 61-65.
3. P. Mittal, R. Singh, A. Sharma, “Deep learning-based object detection in low-altitude UAV datasets: a survey,” Image and Vision Computing, 104, 104046, 2020.
4. M. Bakirci, "A drone-based approach to enhance spatial insight into surrounding air pollutant distributions for healthier indoor environments," Journal of Building Engineering, vol. 87, no. 109023, 2024. https://doi.org/10.1016/j.jobe.2024.109023
5. M. Bakirci, "Efficient air pollution mapping in extensive regions with fully autonomous unmanned aerial vehicles: A numerical perspective," Science of The Total Environment, vol. 909, no. 168606, 2024. https://doi.org/10.1016/j.scitotenv.2023.168606
6. M. Bakirci, "Enhancing air pollution mapping with autonomous UAV networks for extended coverage and consistency," Atmospheric Research, vol. 306, no. 107480, 2024. https://doi.org/10.1016/j.atmosres.2024.107480
7. M. Bakirci, I. Bayraktar, "Harnessing UAV technology and YOLOv9 algorithm for real-time forest fire detection," 2024 International Russian Automation Conference (RusAutoCon), pp. 95-100, Sochi, Russian Federation, 2024. https://doi.org/10.1109/RusAutoCon61949.2024.10694663
8. M. Bakirci, B. Toptas, "Kinematics and autoregressive model analysis of a differential drive mobile robot," 4th International Congress on Human-Computer Interaction, Optimization and Robotic Applications (HORA), pp. 1-6., Ankara, Turkey, 2022. https://doi.org/10.1109/HORA55278.2022.9800071
9. Girshick, R. (2015). Fast R-CNN. ArXiv. https://arxiv.org/abs/1504.08083
10. Ren, S., He, K., Girshick, R., & Sun, J. (2015). Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks. ArXiv. https://arxiv.org/abs/1506.01497
11. Liu, W., Anguelov, D., Erhan, D., Szegedy, C., Reed, S., Fu, C., & Berg, A. C. (2015). SSD: Single Shot MultiBox Detector. ArXiv. https://doi.org/10.1007/978-3-319-46448-0_2
12. Jeong, J., Park, H., & Kwak, N. (2017). Enhancement of SSD by concatenating feature maps for object detection. ArXiv. https://arxiv.org/abs/1705.09587
13. Duan, K., Bai, S., Xie, L., Qi, H., Huang, Q., & Tian, Q. (2019). CenterNet: Keypoint Triplets for Object Detection. ArXiv. https://arxiv.org/abs/1904.08189
14. Redmon, J., Divvala, S., Girshick, R., & Farhadi, A. (2015). You Only Look Once: Unified, Real-Time Object Detection. ArXiv. https://arxiv.org/abs/1506.02640
15. P. Soviany and R. T. Ionescu, "Optimizing the Trade-Off between Single-Stage and Two-Stage Deep Object Detectors using Image Difficulty Prediction," 2018 20th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing (SYNASC), Timisoara, Romania, 2018, pp. 209-214.
16. T. Diwan, G. Anirudh, J.V. Tembhurne, "Object detection using YOLO: challenges, architectural successors, datasets and applications," Multimed Tools Appl, vol. 82, pp. 9243–9275, 2023. https://doi.org/10.1007/s11042-022-13644-y
17. Bochkovskiy, A., Wang, C., & Liao, H. (2020). YOLOv4: Optimal Speed and Accuracy of Object Detection. ArXiv. https://arxiv.org/abs/2004.10934
18. R. Niu, Y. Qu and Z. Wang, "UAV Detection Based on Improved YOLOv4 Object Detection Model," 2021 2nd International Conference on Big Data & Artificial Intelligence & Software Engineering (ICBASE), Zhuhai, China, 2021, pp. 25-29.
19. Wang, C., Yeh, I., & Liao, H. (2024). YOLOv9: Learning What You Want to Learn Using Programmable Gradient Information. ArXiv. https://arxiv.org/abs/2402.13616
20. M. Bakirci, I. Bayraktar, "Transforming aircraft detection through LEO satellite imagery and YOLOv9 for improved aviation safety," 2024 26th International Conference on Digital Signal Processing and its Applications (DSPA), pp. 1-6, Moscow, Russian Federation, 2024. ttps://doi.org/10.1109/DSPA60853.2024.10510106

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