Object Detection Techniques in UAV Aerial Imaging Scenarios

DOI

10.2991/978-94-6463-986-5_59How to use a DOI?

Keywords

Computer Vision Technology; Deep Learning; Object Detection

Abstract

With the rapid development of computer vision technology, object detection in drone-captured imagery has become a highly important research topic in recent years. This technology is now applied in a wide range of fields, including urban planning, agricultural monitoring, disaster response, traffic management, and military reconnaissance. However, using drones for object detection still presents several major challenges. Targets in these images are often very small, backgrounds are typically complex and contain many distracting elements, and the sizes of objects can vary significantly. Additionally, lighting and weather conditions frequently change, and occlusion is a common issue. All of these factors make it difficult to achieve accurate and efficient object detection. This paper begins by introducing two primary categories of object detection models based on deep learning: single-stage and two-stage models. It then examines the main technical challenges encountered in real-world drone-based detection and proposes strategies for improvement. Finally, drawing on recent research, the paper offers recommendations for further enhancing object detection methods.

Copyright

© 2026 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Zhaojie Yao
PY  - 2026
DA  - 2026/02/18
TI  - Object Detection Techniques in UAV Aerial Imaging Scenarios
BT  - Proceedings of the 2025 International Conference on Electronics, Electrical and Grid Technology (ICEEGT 2025)
PB  - Atlantis Press
SP  - 578
EP  - 586
SN  - 2352-5401
UR  - https://doi.org/10.2991/978-94-6463-986-5_59
DO  - 10.2991/978-94-6463-986-5_59
ID  - Yao2026
ER  -