Orientation robust object detection in aerial images using deep convolutional neural network
Abstract
Detecting objects in aerial images is challenged by variance of object colors, aspect ratios, cluttered backgrounds, and in particular, undetermined orientations. In this paper, we propose to use Deep Convolutional Neural Network (DCNN) features from combined layers to perform orientation robust aerial object detection. We explore the inherent characteristics of DC-NN as well as relate the extracted features to the principle of disentangling feature learning. An image segmentation based approach is used to localize ROIs of various aspect ratios, and ROIs are further classified into positives or negatives using an SVM classifier trained on DCNN features. With experiments on two datasets collected from Google Earth, we demonstrate that the proposed aerial object detection approach is simple but effective.
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The most straightforward solution to rotation is to perform data augmentation so that an object in any orientation can be well covered by the augmented data distribution.
“The most straightforward solution to this problem is to perform data augmentation so that an object in any orientation can be well covered by the augmented data distribution [166]”
From Object Detection in 20 Years: A Survey (2023) · cites this paper as [166]
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- Authors
- Zhu, H., Chen, X., Dai, W., Fu, K., Ye, Q., & Jiao, J.
- Affiliations
- University of Chinese Academy of Sciences (CN)
- Chinese Academy of Sciences (CN)
- Venue
- 2015 IEEE International Conference on Image Processing (ICIP)
- Publisher
- IEEE
- Published
- 2015-09
- Pages
- 3735-3739
- ISSN
- 1522-4880 (ISSN-L)
- OpenAlex
- W2296151615
- Cited by
- 483
- References
- 24
- Citations by year
- 2026: 29 · 2025: 66 · 2024: 67 · 2023: 90 · 2022: 67 · 2021: 64 · 2020: 41 · 2019: 39 · 2018: 11 · 2017: 7 · 2016: 2
- FWCI
- 2.73
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- Proceedings - International Conference on Image Processing (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Image and Video Retrieval Techniques
- Physical Sciences › Engineering › Media Technology › Remote-Sensing Image Classification
- Keywords
- aerial object detection, deep convolutional neural network features, image segmentation, region of interest localization, aspect ratio variation, background clutter, support vector machine classification, Google Earth imagery
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