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A Novel Cross Frequency-Domain Interaction Learning for Aerial Oriented Object Detection

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Pattern Recognition and Computer Vision (PRCV 2023)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 14428))

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Abstract

Aerial oriented object detection is a vital task in computer vision, receiving significant attention for its role in remote image understanding. However, most Convolutional Neural Networks (CNNs) methods easily ignore the frequency domain because they only focus on the spatial/channel interaction. To address these limitations, we propose a novel approach called Cross Frequency-domain Interaction Learning (CFIL) for aerial oriented object detection. Our method consists of two modules: the Extraction of Frequency-domain Features (EFF) module and the Interaction of Frequency-domain Features (IFF) module. The EFF module extracts frequency-domain information from the feature maps, enhancing the richness of feature information across different frequencies. The IFF module facilitates efficient interaction and fusion of the frequency-domain feature maps obtained from the EFF module across channels. Finally, these frequency-domain weights are combined with the time-domain feature maps. By enabling full and efficient interaction and fusion of EFF feature weights across channels, the IFF module ensures effective utilization of frequency-domain information. Extensive experiments are conducted on the DOTA V1.0, DOTA V1.5, and HRSC2016 datasets to demonstrate the competitive performance of the proposed CFIL in the aerial oriented object detection. Our code and models will be publicly released.

This work was supported by the Natural Science Foundation of Fujian Province of China under Grant 2022J011271, the Foundation of Educational and Scientific Research Projects for Young and Middle-aged Teachers of Fujian Province under Grant JAT200471, as well as the High-level Talent Project of Xiamen University of Technology under Grant YKJ20013R.

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Author information

Authors and Affiliations

  1. School of Opto-Electronic and Communication Engineering, Xiamen University of Technology, Xiamen, 361024, China

    Weijie Weng, Weiming Lin & Feng Lin

  2. China Telecom Co., Ltd., Beijing, China

    Junchi Ren

  3. Tencent AI Lab, Shenzhen, China

    Fei Shen

Authors
  1. Weijie Weng
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  2. Weiming Lin
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  3. Feng Lin
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  4. Junchi Ren
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  5. Fei Shen
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Corresponding author

Correspondence to Weiming Lin .

Editor information

Editors and Affiliations

  1. Nanjing University of Information Science and Technology, Nanjing, China

    Qingshan Liu

  2. Xiamen University, Xiamen, China

    Hanzi Wang

  3. Beijing University of Posts and Telecommunications, Beijing, China

    Zhanyu Ma

  4. Sun Yat-sen University, Guangzhou, China

    Weishi Zheng

  5. Peking University, Beijing, China

    Hongbin Zha

  6. Chinese Academy of Sciences, Beijing, China

    Xilin Chen

  7. Chinese Academy of Sciences, Beijing, China

    Liang Wang

  8. Xiamen University, Xiamen, China

    Rongrong Ji

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© 2024 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

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Weng, W., Lin, W., Lin, F., Ren, J., Shen, F. (2024). A Novel Cross Frequency-Domain Interaction Learning for Aerial Oriented Object Detection. In: Liu, Q., et al. Pattern Recognition and Computer Vision. PRCV 2023. Lecture Notes in Computer Science, vol 14428. Springer, Singapore. https://doi.org/10.1007/978-981-99-8462-6_24

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  • DOI: https://doi.org/10.1007/978-981-99-8462-6_24

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-99-8461-9

  • Online ISBN: 978-981-99-8462-6

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