[Paper] Deep Learning based Hierarchical Object Detection System Adopting a Depth Correction Scheme for High-Resolution Aerial Images Towards Realization of Autonomous UAV Flight
Author:
Affiliation:
1. Graduate School of Engineering and Technology, Nihon University
2. Faculity of Engineering, Osaka Sangyo University
3. College of Engineering, Nihon University
Publisher
Institute of Image Information and Television Engineers
Subject
Computer Graphics and Computer-Aided Design,Media Technology,Signal Processing
Link
https://www.jstage.jst.go.jp/article/mta/12/1/12_85/_pdf
Reference24 articles.
1. 1) M. Masmoudi, H. Ghazzai, M. Frikha and Y. Massoud: "Object Detection Learning Techniques for Autonomous Vehicle Applications," 2019 IEEE International Conference on Vehicular Electronics and Safety (ICVES), pp.1-5 (Sep. 2019)
2. 2) A. Sarda, S. Dixit and A. Bhan: "Object Detection for Autonomous Driving using YOLO [You Only Look Once] algorithm," 2021 Third international conference on intelligent communication technologies and virtual mobile networks (ICICV), pp.1370-1374 (Feb. 2021)
3. 3) D. Feng, A. Harakeh, S.L. Waslander and K. DietMay er: "A Review and Comparative Study on Probabilistic Object Detection in Autonomous Driving," IEEE Transactions on Intelligent Transportation Systems,23, 8, pp.9961-9980 (Aug. 2021)
4. 4) J. Redmon, S. Divvala, R. Girshick and A. Farhadi: "You Only Look Once: Unified, Real-Time Object Detection," Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp.779-788 (2016)
5. 5) W. Liu, D. Anguelov, D. Erhan, C. Szegedy, S. Reed, C.-Y. Fu and A.C. Berg: "SSD: Single Shot MultiBox Detector," Computer Vision-ECCV 2016: 14th European Conference,14, pp.21-37 (Oct. 2016)
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