Journal of Applied Optics, Volume. 44, Issue 3, 595(2023)
Detection network of critical parts for remote sensing ship based on semantic features
[1] [1] HUANG Z. A sea-land segmentation algorithm based on graph theory[C]//2nd ISPRS International Conference on Computer Vision in Remote Sensing (CVRS 2015).Xiamen, China: ISPRS, 2016: 990111.
[2] [2] XU J, FU K, SUN X. An invariant generalized Hough transform based method of inshore ships detection[C]//2011 International Symposium on Image and Data Fusion. Tengchong, China: IEEE, 2011: 1-4.
[8] [8] GIRSHICK R. Fast R-CNN[C]//2015 IEEE International Conference on Computer Vision (ICCV).Santiago, Chile: IEEE, 2015: 1440-1448.
[10] [10] HE K, GKIOXARI G, DOLL A R P, et al. Mask r-cnn[C]//Proceedings of the IEEE International Conference on Computer Vision. Venice, Italy: IEEE, 2017: 2961-2969.
[11] [11] LIU W, ANGUELOV D, ERHAN D, et al. SSD: single shot multibox detector[C]// ECCV 2016: Computer Vision – ECCV 2016. Amsterdam, The Netherlands: Springer C, 2016: 21-3 7.
[12] [12] REDMON J, FARHADI A. YOLO9000: better, faster, stronger[C]//2017 IEEE Conference on Computer Vision and Pattern Rattern Recognition(CVPR) Honolulu.HI, USA: IEEE, 2017: 7263-7271.
[13] [13] LIN T Y, GOYAL P, GIRSHICK R, et al. Focal loss for dense object detection[C]//Proceeding of the IEEE International Conference on Computer Vision. Piscataway: IEEE, 2017: 2980-2988.
[14] [14] LIU Z, HU J, WENG L, et al. Rotated region based CNN for ship detection[C] //2017 IEEE International Conference on Image Processing (ICIP).Beijing, China: IEEE, 2017: 900-904.
[15] [15] DING J, XUE N, LONG Y, et al. Learning RoI transformer for oriented object detection in aerial images[C]//2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Long Beach, CA. USA: IEEE, 2019: 2844-2853.
[16] [16] YANG X,YAN J,FENG Z, et al. R3det: refined single-stage detector with feature refinement for rotating object[C]// AAAI conference on artificial intelliignce Palo Alto California USA:AAAI,2021,35(4):3163-3171.
[17] [17] HAN Zishuo, WANG Chunping, FU Qiang, et al. Ship detection in SAR images based on super dense feature pyramid networks[J]. Systems Engineering and Electronics. 2020, 42(10): 2214-2222.
[18] [18] WOO S, PARK J, LEE J, et al. Cbam: convolutional block attention module[C]//ECCV 2018: Computer Vision – ECCV 2018.Munich, Germany: Springer C, 2018: 3-19.
[19] [19] HE K, ZHANG X, REN S, et al. Deep residual learning for image recognition[C]//2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Las Vegas, NV, USA: IEEE, 2016: 770-778.
[20] [20] ZEILER M D, TAYLOR G W, FERGUS R. Adaptive deconvolutional networks for mid and high level feature learning[C]//2011 International Conference on Computer Vision. Barcelona, Spain: IEEE, 2011: 2018-2025.
[21] [21] LIU Z, YUAN L, WENG L, et al. A high resolution optical satellite image dataset for ship recognition and some new baselines[C]//6th International Conference on Pattern Recognition Applications and Methods(ICPRAM 2017).Porto,Portugal:SCITE,2017:324-331.
[23] [23] XIA G, BAI X, DING J, et al. DOTA: a large-scale dataset for object detection in aerial images[C]//2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.Salt Lake City, UT, USA: IEEE, 2018: 3974-3983.
[24] [24] DENG J, DONG W, SOCHER R, et al. ImageNet: a large-scale hierarchical image database[C]//2009 IEEE Conference on Computer Vision and Pattern Recognition. Miami, FL, USA: IEEE, 2009: 248-255.
[25] [25] HAN J, DING J, XUE N, et al. ReDet: A rotation-equivariant detector for aerial object detection[C]//2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).Nashville, TN, USA: IEEE, 2021: 2785-2794.
[26] [26] ZHANG H, CHANG H, MA B, et al. Dynamic R-CNN: towards high quality object detection via dynamic training[C]//ECCV 2020: Computer Vision – ECCV 2020.Glasgow, UK: Springer C, 2020: 260-275.
[27] [27] XIE X, CHENG G, WANG J, et al. Oriented R-CNN for object detection[C]//2021 IEEE/CVF International Conference on Computer Vision (ICCV).Montreal, QC, Canada: IEEE, 2021: 3500-3509.
[28] [28] WANG J T, XIAO W, NI T W. Efficient object detection method based on improved YOLOv3 network for remote sensing images[C]//2020 3rd International Conference on Artificial Intelligence and Big Data (ICAIBD). Chengdu, China: IEEE, 2020: 242-246.
[29] [29] GUO Z, LIU C, ZHANG X, et al. Beyond bounding-box: convex-hull feature adaptation for oriented and densely packed object detection[C]//2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). Nashville, TN, USA: IEEE, 2021: 8788-8797.
Get Citation
Copy Citation Text
Dongdong ZHANG, Chunping WANG, Qiang FU. Detection network of critical parts for remote sensing ship based on semantic features[J]. Journal of Applied Optics, 2023, 44(3): 595
Category: Research Articles
Received: Jun. 6, 2022
Accepted: --
Published Online: Jun. 19, 2023
The Author Email: FU Qiang (1418748495@qq.com)