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Concatenate and Shuffle Network: A Real-Time Underwater Object Detector for Small and Dense Objects

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Proceedings of 2021 International Conference on Autonomous Unmanned Systems (ICAUS 2021) (ICAUS 2021)

Part of the book series: Lecture Notes in Electrical Engineering ((LNEE,volume 861))

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Abstract

Object detection of underwater optical images is of great significance in many underwater missions, such as the salvage of underwater objects, the exploration of marine organisms, etc. However, underwater objects are often small and dense, which are difficult to detect. To tackle above issues, we propose a novel framework of underwater object detection named Concatenate and Shuffle Network (CSNet) based on center points detection, which can not only detect small and dense objects with high accuracy, but also detect in real time. Firstly, a multi-scale fusion strategy called Feature Concatenation Shuffle (FCS) is proposed. The detailed features from shallow layer in Convolutional Neural Network are completely integrated into deep layer, and the capability for extracting features of small objects is enhanced. Moreover, to accelerate our method, we propose a lightweight deconvolution block (DB), which integrates a structure of dual-branch feature fusion and a lightweight deconvolution method. In addition, we study the advantages of detecting dense objects based on center points and introduce it to our detector. Lastly, experiments show that CSNet achieves the best speed-accuracy trade-off on URPC 2018 with 39.7% AP at 58.8 FPS and 42.4% AP with multi-scale testing at 5.7 FPS. Compared with several state-of-the-art detectors, CSNet reaches a competitive accuracy at a breakthrough speed and can run in real time under various computing conditions.

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Authors and Affiliations

  1. School of Marine Science and Technology, Northwestern Polytechnical University, Xian, 710072, China

    Xuyang Jiang

  2. Unmanned System Research Institute, Northwestern Polytechnical University, Xian, 710072, China

    Zhaoyong Mao & Junge Shen

Authors
  1. Xuyang Jiang
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  2. Zhaoyong Mao
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  3. Junge Shen
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Corresponding author

Correspondence to Junge Shen .

Editor information

Editors and Affiliations

  1. College of Intelligence Science and Technology, National University of Defense Technology, Changsha, Hunan, China

    Meiping Wu

  2. College of Intelligence Science and Technology, National University of Defense Technology, Changsha, Hunan, China

    Yifeng Niu

  3. Beijing HIWING Scientific and Technological Information Institute, Beijing, China

    Mancang Gu

  4. Science and Technology on Complex System Control and Intelligent Agent Cooperative Laboratory, Beijing, China

    Jin Cheng

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Jiang, X., Mao, Z., Shen, J. (2022). Concatenate and Shuffle Network: A Real-Time Underwater Object Detector for Small and Dense Objects. In: Wu, M., Niu, Y., Gu, M., Cheng, J. (eds) Proceedings of 2021 International Conference on Autonomous Unmanned Systems (ICAUS 2021). ICAUS 2021. Lecture Notes in Electrical Engineering, vol 861. Springer, Singapore. https://doi.org/10.1007/978-981-16-9492-9_64

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