Acta Optica Sinica, Volume. 43, Issue 12, 1212001(2023)

Lightweight Ship Detection Based on Optical Remote Sensing Images for Embedded Platform

Huiying Wang1... Chunping Wang1, Qiang Fu1,*, Zishuo Han2 and Dongdong Zhang1 |Show fewer author(s)
Author Affiliations
  • 1Department of Electronic and Optical Engineering, Shijiazhuang Campus of Army Engineering University, Shijiazhuang 050003, Hebei, China
  • 232356 Troops of the Chinese People's Liberation Army, Xining 710003, Qinghai, China
  • show less
    References(33)

    [1] Xue J D, Zhu J J, Zhang J et al. Object detection in optical remote sensing images based on FFC-SSD model[J]. Acta Optica Sinica, 42, 1210002(2022).

    [2] Nong Y J, Wang J J. Remote sensing image caption method based on attention and reinforcement learning[J]. Acta Optica Sinica, 41, 2228001(2021).

    [3] Dai H, Du L, Wang Y et al. A modified CFAR algorithm based on object proposals for ship target detection in SAR images[J]. IEEE Geoscience and Remote Sensing Letters, 13, 1925-1929(2016).

    [4] Tao D, Anfinsen S N, Brekke C. Robust CFAR detector based on truncated statistics in multiple-target situations[J]. IEEE Transactions on Geoscience and Remote Sensing, 54, 117-134(2016).

    [5] Zhang D D, Wang C P, Fu Q. CAFC-net: a critical and align feature constructing network for oriented ship detection in aerial images[J]. Computational Intelligence and Neuroscience, 2022, 3391391(2022).

    [6] He K M, Zhang X Y, Ren S Q et al. Spatial pyramid pooling in deep convolutional networks for visual recognition[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 37, 1904-1916(2015).

    [7] Girshick R. Fast R-CNN[C], 1440-1448(2015).

    [8] Ren S Q, He K M, Girshick R et al. Faster R-CNN: towards real-time object detection with region proposal networks[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 39, 1137-1149(2017).

    [9] Dai J F, Li Y, He K et al. R-FCN: object detection via region-based fully convolutional networks[C], 379-387(2016).

    [10] Liu W, Anguelov D, Erhan D et al. SSD: single shot multibox detector[M]. Leibe B, Matas J, Sebe N, et al. Computer vision-ECCV 2016. Lecture notes in computer science, 9905, 21-37(2016).

    [11] Redmon J, Farhadi A. YOLO9000: better, faster, stronger[C], 6517-6525(2017).

    [13] Redmon J, Divvala S, Girshick R et al. You only look once: unified, real-time object detection[C], 779-788(2016).

    [15] Cui J H, Zhang Y Z, Wang Z et al. Light-weight object detection networks for embedded platform[J]. Acta Optica Sinica, 39, 0415006(2019).

    [16] Nong Y J, Wang J J. Real-time object detection in remote sensing images based on embedded system[J]. Acta Optica Sinica, 41, 1028001(2021).

    [17] Yan B, Fan P, Lei X Y et al. A real-time apple targets detection method for picking robot based on improved YOLOv5[J]. Remote Sensing, 13, 1619(2021).

    [18] Liu T, Zhou B J, Zhao Y S et al. Ship detection algorithm based on improved YOLO V5[C], 483-487(2021).

    [19] Zhang M H, Xu S B, Song W et al. Lightweight underwater object detection based on YOLO v4 and multi-scale attentional feature fusion[J]. Remote Sensing, 13, 4706(2021).

    [20] Hu J M, Zhi X Y, Shi T J et al. PAG-YOLO: a portable attention-guided YOLO network for small ship detection[J]. Remote Sensing, 13, 3059(2021).

    [21] Liu Y, Gao M F, Zong H M et al. Real-time object detection for the running train based on the improved YOLO V4 neural network[J]. Journal of Advanced Transportation, 2022, 4377953(2022).

    [22] Chollet F. Xception: deep learning with depthwise separable convolutions[C], 1800-1807(2017).

    [24] Sandler M, Howard A, Zhu M L et al. MobileNetV2: inverted residuals and linear bottlenecks[C], 4510-4520(2018).

    [25] Zhang X Y, Zhou X Y, Lin M X et al. ShuffleNet: an extremely efficient convolutional neural network for mobile devices[C], 6848-6856(2018).

    [26] Ma N N, Zhang X Y, Zheng H T et al. ShuffleNet V2: practical guidelines for efficient CNN architecture design[M]. Ferrari V, Hebert M, Sminchisescu C, et al. Computer Vision-ECCV 2018. Lecture Notes in Computer Science, 11218, 122-138(2018).

    [28] Hu J, Shen L, Sun G. Squeeze-and-excitation networks[C], 7132-7141(2018).

    [30] Woo S, Park J, Lee J Y et al. Cbam: convolutional block attention module[M]. Ferrari V, Hebert M, Sminchisescu C, et al. Computer Vision-ECCV 2018. Lecture Notes in Computer Science, 11211, 3-19(2018).

    [31] Hou Q B, Zhou D Q, Feng J S. Coordinate attention for efficient mobile network design[C], 13708-13717(2021).

    [32] Bukhsh Z A, Jansen N, Saeed A. Damage detection using in-domain and cross-domain transfer learning[J]. Neural Computing and Applications, 33, 16921-16936(2021).

    [33] Liu Z K, Yuan L, Weng L B et al. A high resolution optical satellite image dataset for ship recognition and some new baselines[C], 324-331(2017).

    Tools

    Get Citation

    Copy Citation Text

    Huiying Wang, Chunping Wang, Qiang Fu, Zishuo Han, Dongdong Zhang. Lightweight Ship Detection Based on Optical Remote Sensing Images for Embedded Platform[J]. Acta Optica Sinica, 2023, 43(12): 1212001

    Download Citation

    EndNote(RIS)BibTexPlain Text
    Save article for my favorites
    Paper Information

    Category: Instrumentation, Measurement and Metrology

    Received: Sep. 7, 2022

    Accepted: Oct. 27, 2022

    Published Online: Apr. 25, 2023

    The Author Email: Qiang Fu (1245316750@qq.com)

    DOI:10.3788/AOS221689

    Topics