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Jun. 2021
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Zongling Li, Qingjun Zhang, Teng Long, Baojun Zhao. Ship Target Detection and Recognition Method on Sea Surface Based on Multi-Level Hybrid Network[J]. JOURNAL OF BEIJING INSTITUTE OF TECHNOLOGY, 2021, 30(zk): 1-10. doi: 10.15918/j.jbit1004-0579.20141
Citation: Zongling Li, Qingjun Zhang, Teng Long, Baojun Zhao. Ship Target Detection and Recognition Method on Sea Surface Based on Multi-Level Hybrid Network[J].JOURNAL OF BEIJING INSTITUTE OF TECHNOLOGY, 2021, 30(zk): 1-10.doi:10.15918/j.jbit1004-0579.20141
shu

Ship Target Detection and Recognition Method on Sea Surface Based on Multi-Level Hybrid Network

doi:10.15918/j.jbit1004-0579.20141
  • 1.

    School of Information and Electronics, Beijing Institute of Technology, Beijing 100081, China

  • 2.

    Institute of Spacecraft System Engineering, China Academy of Space Technology, Beijing 100094, China

Funds:the National Natural Science Foundation of China (91438203)
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  • Corresponding author:professor, Ph.D. E-mail:zbj@bit.edu.cn
  • Received Date:2020-11-01
  • Publish Date:2021-06-30
    Abstract
  • This paper proposes a method of ship detection and recognition based on a multi-level hybrid network, designing a noise reducing and smoothing image enhancement algorithm based on multi-level two-dimensional template filter and three-layer pyramid structure. This work constructs an adaptive segmentation detection and ultra-lightweight target classification network model combining global and local image gray statistics. With a combination of traditional image processing and deep learning methods, the demand for computing and storage resources is reduced greatly. This method can detect and recognize the ship targets near the sea-sky-level quickly and has been verified by real flight camera data, and the accuracy rate is more than 90%. In comparison to the Tiny YOLOV3 network, the accuracy rate is reduced by 5%, but the calculation efficiency is increased by 50 times, and the parameters are reduced by 550 times.
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    • References (16)
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