[1] R.J. Koester, Lost Person Behavi: A Search Rescue. lottesville, USA: DBS Productions LLC, 2008.
[2] M.B. Bejiga, A. Zeggada, F. Melgani, Convolutional neural wks f near realtime object detection from UAV imagery in avalanche search rescue operations, in: Proc. Of IEEE Intl. Geoscience Remote Sensing Symposium, Beijing, 2016, pp. 693–696.
[3] Martinez-Alpiste I., Golcarenarenji G., Wang Q., Alcaraz-Calero J.M.. Search and rescue operation using UAVs: A case study. Expert Syst. Appl., 178, 114937:1-9(2021).
[4] M. Kampouraki, G.A. Wood, T.R. Brewer, Opptunities limitations of object based image analysis f detecting urban impervious vegetated surfaces using truecolour aerial photography, in: T. Blhke, S. Lang, G.J. Hay (Eds.), ObjectBased Image Analysis: Spatial Concepts f KnowledgeDriven Remote Sensing Applications, Springer, Berlin, 2008, pp. 555–569.
[5] C.A.B. Baker, S. Ramchurn, W.T.L.Teacy, N.R. Jennings, Planning search rescue missions f UAV teams, in: Proc. of 21nd European Conf. on Artificial Intelligence, Hague, 2016, pp. 1777–1778.
[6] K. Yun, L. Nguyen, T. Nguyen, et al., Small target detection f search rescue operations using distributed deep learning synthetic data generation, in: Proc. of SPIE 10995, Baltime, 2019, pp. 1099507:1–6.
[7] S.O. Murphy, C. Sreenan, K.N. Brown, Autonomous unmanned aerial vehicle f search rescue using software defined radio, in: Proc. of IEEE 89th Vehicular Technology Conf., Kuala Lumpur, 2019, pp. 1–6.
[8] Lygouras E., Santavas N., Taitzoglou A., Tarchanidis K., Mitropoulos A., Gasteratos A.. Unsupervised human detection with an embedded vision system on a fully autonomous UAV for search and rescue operations. Sensors,, 19, 3542:1-20(2019).
[11] T.Y. Lin, P. Goyal, R. Girshick, K.M. He, P. Dollár, Focal loss f dense object detection, in: Proc. of IEEE Intl. Conf. on Computer Vision, Venice, 2017, pp. 2999–3007.
[12] Z.W. Cai, N. Voncelos, Cade RCNN: Delving into high quality object detection, in: Proc. of IEEECVF Conf. on Computer Vision Pattern Recognition, Salt Lake City, 2018, pp. 6154–6162.
[13] J. Redmon, S. Divvala, R. Girshick, A. Farhadi, You only look once: Unified, realtime object detection, in: Proc. of IEEE Conf. on Computer Vision Pattern Recognition, Las Vegas, 2016, pp. 779–788.
[14] W. Liu, D. Anguelov, D. Erhan, et al., SSD: Single shot MultiBox detect, in: Proc. of 14th European Conf. on Computer Vision, Amsterdam, 2016, pp. 21–37.
[15] Ali Z., Memon Q.. Time delay tracking for multiuser synchronization in CDMA networks. Journal of Networks,, 8, 1929-1935(2013).
[16] M. Tan, R. Pang, Q.V. Le, EfficientDet: Scalable efficient object detection, in: Proc. of IEEECVF Conf. on Computer Vision Pattern Recognition, Seattle, 2020, pp. 10778–10787.
[17] M. Tan, Q.V. Le, Efficient: Rethinking model scaling f convolutional neural wks, in: Proc. of the 36th Intl. Conf. Machine Learning, Long Beach, 2019.
[18] J. Deng, W. Dong, R. Socher, L.J. Li, K. Li, L. FeiFei, Image: A largescale hierarchical image database, in: Proc. of IEEE Conf. on Computer Vision Pattern Recognition, Miami, 2009, pp. 248–255.
[19] T.Y. Lin, M. Maire, S. Belongie, et al., Microsoft COCO: Common objects in context, in: Proc. of 13th European Conf. on Computer Vision, Zurich, 2014, pp. 740–755.
[20] Rostami M., Kolouri S., Eaton E., Kim K.. Deep transfer learning for few-shot SAR image classification. Remote Sensing, 11, 1374:1-19(2019).
[21] Zhang X.-D., Zhu K., Chen G.-Z. et al. Geospatial object detection on high resolution remote sensing imagery based on double multi-scale feature pyramid network. Remote Sensing,, 11, 755:1-27(2019).
[22] S. Sambolek, M. IvasicKos, Automatic person detection in search rescue operations using deep CNN detects, IEEE Access 9 (2021) 37905−37922.
[23] Vasić M.K., Papić V.. Improving the model for person detection in aerial image sequences using the displacement vector: A search and rescue scenario. Drones, 6, 19:1-19(2022).
[24] M.G. Drer, A.E. Tolmacheva, Comparison of the YOLOv3 mask RCNN architectures’ efficiency in the smart refrigerat’s computer vision, Journal of Physics, Conference Series, 1679 (4) (Nov. 2020) 042022:1–12.
[25] J.A. Kim, J.Y. Sung, S.H. Park, Comparison of fasterRCNN, YOLO, SSD f realtime vehicle type recognition, in: Proc. IEEE Intl. Conf. on Consumer Electronics Asia, Seoul, 2020, pp. 1–4.
[26] Mao Y.-T.. A pedestrian detection algorithm for low light and dense crowd based on improved YOLO algorithm. MATEC Web of Conf., 355, 03020:1-16(2022).
[27] C. Wang, W. He, Y. Nie, et al. GoldYOLO: Efficient object detect via gatherdistribute mechanism, in: Proc. of 37th Conf. on Neural Infmation Processing Systems, New leans, 2023.
[28] Learn OpenCV, YOLOv5: Expert guide to custom object detection training [Online]. Available, https:learnopencv.comcustomobjectdetectiontrainingusingyolov5, May 2023.
[29] Paperspace by DigitalOcean, How to train YOLO v5 on a custom dataset [Online]. Available, https:blog.paperspace.comtrainyolov5customdata , May 2023.
[30] F. Bashir, F. Pikli, Perfmance evaluation of object detection tracking systems, in: Proc. of 9th IEEE Intl. Wkshop on PETS, New Yk, 2006, pp. 7–14.
[31] N. Bachir, Q. Memon, Investigating YOLOv5 f search rescue operations involving UAVs: Investigating YOLO5, in: Proc. of 5th Intl. Conf. on Control Computer Vision, Xiamen, 2022, pp. 200–204.
[32] S. Caputo, G. Castellano, F. Greco, C. Mencar, N. Petti, G. Vessio, Human detection in drone images using YOLO f searchrescue operations, in: Proc. of 20th Intl. Conf. of AIxIA 2021−Advances in Artificial Intelligence, Milan, 2022, pp. 326–337.
