Airport Detection Using End-to-End Convolutional Neural Network with Hard Example Mining Article Swipe
Bowen Cai
,
Zhiguo Jiang
,
Haopeng Zhang
,
Danpei Zhao
,
Yuan Yao
·
YOU?
·
· 2017
· Open Access
·
· DOI: https://doi.org/10.3390/rs9111198
YOU?
·
· 2017
· Open Access
·
· DOI: https://doi.org/10.3390/rs9111198
Deep convolutional neural network (CNN) achieves outstanding performance in the field of target detection. As one of the most typical targets in remote sensing images (RSIs), airport has attracted increasing attention in recent years. However, the essential challenge for using deep CNN to detect airport is the great imbalance between the number of airports and background examples in large-scale RSIs, which may lead to over-fitting. In this paper, we develop a hard example mining and weight-balanced strategy to construct a novel end-to-end convolutional neural network for airport detection. The initial motivation of the proposed method is that backgrounds contain an overwhelming number of easy examples and a few hard examples. Therefore, we design a hard example mining layer to automatically select hard examples by their losses, and implement a new weight-balanced loss function to optimize CNN. Meanwhile, the cascade design of proposal extraction and object detection in our network releases the constraint on input image size and reduces spurious false positives. Compared with geometric characteristics and low-level manually designed features, the hard example mining based network could extract high-level features, which is more robust for airport detection in complex environment. The proposed method is validated on a multi-scale dataset with complex background collected from Google Earth. The experimental results demonstrate that our proposed method is robust, and superior to the state-of-the-art airport detection models.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/rs9111198
- https://www.mdpi.com/2072-4292/9/11/1198/pdf?version=1511425391
- OA Status
- gold
- Cited By
- 39
- References
- 37
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W2769756082
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W2769756082Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.3390/rs9111198Digital Object Identifier
- Title
-
Airport Detection Using End-to-End Convolutional Neural Network with Hard Example MiningWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2017Year of publication
- Publication date
-
2017-11-21Full publication date if available
- Authors
-
Bowen Cai, Zhiguo Jiang, Haopeng Zhang, Danpei Zhao, Yuan YaoList of authors in order
- Landing page
-
https://doi.org/10.3390/rs9111198Publisher landing page
- PDF URL
-
https://www.mdpi.com/2072-4292/9/11/1198/pdf?version=1511425391Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
-
https://www.mdpi.com/2072-4292/9/11/1198/pdf?version=1511425391Direct OA link when available
- Concepts
-
Computer science, Convolutional neural network, End-to-end principle, Deep learning, Data mining, Object detection, Spurious relationship, Artificial intelligence, False positive paradox, Field (mathematics), Attention network, Pattern recognition (psychology), Machine learning, Mathematics, Pure mathematicsTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
39Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 3, 2024: 4, 2023: 1, 2022: 5, 2021: 7Per-year citation counts (last 5 years)
- References (count)
-
37Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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