Journal of Shandong University(Engineering Science) ›› 2022, Vol. 52 ›› Issue (2): 99-106.doi: 10.6040/j.issn.1672-3961.0.2021.329

Previous Articles     Next Articles

Infrared ship segmentation method based on weakly-supervised and semi-supervised learning

YIN Xu1, LIU Zhaoying1, ZHANG Ting1*, LI Yujian1,2   

  1. 1. Faculty of Information Technology, Beijing University of Technology, Beijing 100124, China;
    2. School of Artificial Intelligence, Guilin University of Electronic Technology, Guilin 541004, Guangxi, China
  • Published:2022-04-20

CLC Number: 

  • TP3
[1] LONG J, SHELHAMER E, DARRELL T. Fully convolutional networks for semantic segmentation[C] //Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. New York, USA: IEEE, 2015: 3431-3440.
[2] RONNEBERGER O, FISCHER P, BROX T. U-net: convolutional networks for biomedical image segmentation[C] //Proceedings of the International Conference on Medical Image Computing and Computer-Assisted Intervention. Cham, Switzerland: Springer, 2015: 234-241.
[3] BADRINARAYANAN V, KENDALL A, CIPOLLA R. Segnet: a deep convolutional encoder-decoder architecture for image segmentation[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2017, 39(12): 2481-2495.
[4] LIN G, MILAN A, SHEN C, et al. Refinenet: multi-path refinement networks for high-resolution semantic segmentation[C] //Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. New York, USA: IEEE, 2017: 1925-1934.
[5] CHEN L C, PAPANDREOU G, KOKKINOS I, et al. Deeplab: semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2017, 40(4): 834-848.
[6] ZHAO H, SHI J, QI X, et al. Pyramid scene parsing network[C] //Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. New York, USA: IEEE, 2017: 2881-2890.
[7] CHEN L C, ZHU Y, PAPANDREOU G, et al. Encoder-decoder with atrous separable convolution for semantic image segmentation[C] //Proceedings of the European Conference on Computer Vision. Cham, Switzerland: Spri-nger, 2018: 801-818.
[8] 陈倩. 基于局部区域生长和Faster R-CNN的弱监督图像语义分割[D].合肥: 安徽大学, 2020. CHEN Qian. Weakly supervised image semantic segmentation based on local region growth and faster R-CNN[D]. Hefei: Anhui University, 2020.
[9] ZHOU B, KHOSLA A, LAPEDRIZA A, et al. Learning deep features for discriminative localization[C] //Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. New York, USA: IEEE, 2016: 2921-2929.
[10] SINGH K K, LEE Y J. Hide-and-seek: forcing a network to be meticulous for weakly-supervised object and action localization[C] //Proceedings of the 2017 IEEE International Conference on Computer Vision. New York, USA: IEEE, 2017: 3544-3553.
[11] WEI Y, FENG J, LIANG X, et al. Object region mining with adversarial erasing: a simple classification to semantic segmentation approach[C] //Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. New York, USA: IEEE, 2017: 1568-1576.
[12] MAI J, YANG M, LUO W. Erasing integrated learning: a simple yet effective approach for weakly supervised object localization[C] //Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. New York, USA: IEEE, 2020: 8766-8775.
[13] ZHANG X, WEI Y, FENG J, et al. Adversarial complementary learning for weakly supervised object localization[C] //Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. New York, USA: IEEE, 2018: 1325-1334.
[14] HOU Q, JIANG P T, WEI Y, et al. Self-erasing network for integral object attention[J]. Advances in Neural Information Processing Systems, 2018, 31: 549-559.
[15] KIM D, CHO D, YOO D, et al. Two-phase learning for weakly supervised object localization[C] //Proceedings of the IEEE International Conference on Computer Vision. New York, USA: IEEE, 2017: 3534-3543
[16] WANG X, SHRIVASTAVA A, GUPTA A. A-fast-rcnn: hard positive generation via adversary for object detection[C] //Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. New York, USA: IEEE, 2017: 2606-2615.
[17] WEI Y, XIAO H, SHI H, et al. Revisiting dilated convolution: a simple approach for weakly- and semi-supervised semantic segmentation[C] //Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. New York, USA: IEEE, 2018: 7268-7277.
[18] FAN J, ZHANG Z, TAN T, et al. Cian: cross-image affinity net for weakly supervised semantic segmentation[C] //Proceedings of the AAAI Conference on Arti-ficial Intelligence. Palo Alto, USA: AAAI, 2020, 10762-10769.
[19] FAN R, HOU Q, CHENG M M, et al. Associating inter-image salient instances for weakly supervised semantic segmentation[C] //Proceedings of the European Conference on Computer Vision. Berlin, Germany: Springer, 2018: 367-383.
[20] KOLESNIKOV A, LAMPERT C H. Seed, expand and constrain: three principles for weakly-supervised image segmentation[C] //Proceedings of the European Conference on Computer Vision. Cham, Switzerland: Springer, 2016: 695-711.
[21] HUANG Z, WANG X, WANG J, et al. Weakly-supervised semantic segmentation network with deep seeded region growing[C] //Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. New York, USA: IEEE, 2018: 7014-7023.
[22] WANG X, YOU S, LI X, et al. Weakly-supervised semantic segmentation by iteratively mining common object features[C] //Proceedings of the IEEE Confe-rence on Computer Vision and Pattern Recognition. New York, USA: IEEE, 2018: 1354-1362.
[23] AHN J, KWAK S. Learning pixel-level semantic affinity with image-level supervision for weakly supervised semantic segmentation[C] //Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. New York, USA: IEEE, 2018: 4981-4990.
[24] SELVARAJU R R, COGSWELL M, DAS A, et al. Grad-cam: visual explanations from deep networks via gradient-based localization[C] //Proceedings of the IEEE International Conference on Computer Vision. New York, USA: IEEE, 2017: 618-626.
[25] SOULY N, SPAMPINATO C, SHAH M. Semi supervised semantic segmentation using generative adversarial network[C] //Proceedings of the IEEE International Conference on Computer Vision. New York, USA: IEEE, 2017: 5688-5696.
[26] AI J, CHEN S, DENG P, et al. CycleGANs for semi-supervised defects segmentation[C] //Proceedings of the 2020 International Conference on Sensing, Measurement & Data Analytics in the Era of Artificial Intel-ligence(ICSMD). Xi'an, China: IEEE, 2020: 611-616.
[27] HE K, ZHANG X, REN S, et al. Deep residual learning for image recognition[C] //Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. New York, USA: IEEE, 2016: 770-778.
[28] WU Z, SU L, HUANG Q. Cascaded partial decoder for fast and accurate salient object detection[C] //Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. New York, USA: IEEE, 2019: 3907-3916.
[29] KINGMA D, BA J. Adam: a method for stochastic optimization[C] //Proceedings of the 3rd International Conference on Learning Representations. CA, USA: ICLR, 2015: 1-13.
[30] KRÄHENB(¨overU)HL P, KOLTUN V. Efficient inference in fully connected crfs with gaussian edge potentials[J]. Advances in Neural Information Processing Systems, 2011, 24(24): 109-117.
[1] DENG Bin, ZHANG Zongbao, ZHAO Wenmeng, LUO Xinhang, WU Qiuwei. Cloud-edge collaborative and graph neural network based load forecasting method for electric vehicle charging stations [J]. Journal of Shandong University(Engineering Science), 2025, 55(5): 62-69.
[2] LI Erchao, ZHANG Zhizhao. Online dynamic demand vehicle routing planning [J]. Journal of Shandong University(Engineering Science), 2024, 54(5): 62-73.
[3] YANG Jucheng, WEI Feng, LIN Liang, JIA Qingxiang, LIU Jianzheng. A research survey of driver drowsiness driving detection [J]. Journal of Shandong University(Engineering Science), 2024, 54(2): 1-12.
[4] XIAO Wei, ZHENG Gengsheng, CHEN Yujia. Named entity recognition method combined with self-training model [J]. Journal of Shandong University(Engineering Science), 2024, 54(2): 96-102.
[5] Gang HU, Lemeng WANG, Zhiyu LU, Qin WANG, Xiang XU. Importance identification method based on multi-order neighborhood hierarchical association contribution of nodes [J]. Journal of Shandong University(Engineering Science), 2024, 54(1): 1-10.
[6] Jiachun LI,Bowen LI,Jianbo CHANG. An efficient and lightweight RGB frame-level face anti-spoofing model [J]. Journal of Shandong University(Engineering Science), 2023, 53(6): 1-7.
[7] Yujiang FAN,Huanhuan HUANG,Jiaxiong DING,Kai LIAO,Binshan YU. Resilience evaluation system of the old community based on cloud model [J]. Journal of Shandong University(Engineering Science), 2023, 53(5): 1-9, 19.
[8] Ying LI,Jiankun WANG. The classification of mild cognitive impairment based on supervised graph regularization and information fusion [J]. Journal of Shandong University(Engineering Science), 2023, 53(4): 65-73.
[9] YU Yixuan, YANG Geng, GENG Hua. Multimodal hierarchical keyframe extraction method for continuous combined motion [J]. Journal of Shandong University(Engineering Science), 2023, 53(2): 42-50.
[10] Caihui LIU,Qi ZHOU,Xiaowen YE. An intrusion detection model based on improved ReliefF algorithm [J]. Journal of Shandong University(Engineering Science), 2023, 53(2): 1-10.
[11] HUANG Huajuan, CHENG Qian, WEI Xiuxi, YU Chuchu. Adaptive crow search algorithm with Jaya algorithm and Gaussian mutation [J]. Journal of Shandong University(Engineering Science), 2023, 53(2): 11-22.
[12] CHU Jiajing, PAN Qingxian, PAN Ya'nan, LIU Qingju. Crowdsourcing quality control algorithm based on reputation model [J]. Journal of Shandong University(Engineering Science), 2023, 53(2): 93-101.
[13] ZHANG Hao, LI Ziling, LIU Tong, ZHANG Dawei, TAO Jianhua. A technology prediction model based on fuzzy Bayesian networks with sociological factors [J]. Journal of Shandong University(Engineering Science), 2023, 53(2): 23-33.
[14] WU Yanli, LIU Shuwei, HE Dongxiao, WANG Xiaobao, JIN Di. Poisson-gamma topic model of describing multiple underlying relationships [J]. Journal of Shandong University(Engineering Science), 2023, 53(2): 51-60.
[15] LIU Bin, WANG Lei , WANG Chong, CAI Xiangxiang. An incremental method for updating approximations of consistent blocks while the universe evolves over time [J]. Journal of Shandong University(Engineering Science), 2023, 53(2): 109-117.
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed   
[1] WANG Su-yu,<\sup>,AI Xing<\sup>,ZHAO Jun<\sup>,LI Zuo-li<\sup>,LIU Zeng-wen<\sup> . Milling force prediction model for highspeed end milling 3Cr2Mo steel[J]. JOURNAL OF SHANDONG UNIVERSITY (ENGINEERING SCIENCE), 2006, 36(1): 1 -5 .
[2] LI Kan . Empolder and implement of the embedded weld control system[J]. JOURNAL OF SHANDONG UNIVERSITY (ENGINEERING SCIENCE), 2008, 38(4): 37 -41 .
[3] KONG Xiang-zhen,LIU Yan-jun,WANG Yong,ZHAO Xiu-hua . Compensation and simulation for the deadband of the pneumatic proportional valve[J]. JOURNAL OF SHANDONG UNIVERSITY (ENGINEERING SCIENCE), 2006, 36(1): 99 -102 .
[4] YU Jia yuan1, TIAN Jin ting1, ZHU Qiang zhong2. Computational intelligence and its application in psychology[J]. JOURNAL OF SHANDONG UNIVERSITY (ENGINEERING SCIENCE), 2009, 39(1): 1 -5 .
[5] CHEN Rui, LI Hongwei, TIAN Jing. The relationship between the number of magnetic poles and the bearing capacity of radial magnetic bearing[J]. JOURNAL OF SHANDONG UNIVERSITY (ENGINEERING SCIENCE), 2018, 48(2): 81 -85 .
[6] LI Ke,LIU Chang-chun,LI Tong-lei . Medical registration approach using improved maximization of mutual information[J]. JOURNAL OF SHANDONG UNIVERSITY (ENGINEERING SCIENCE), 2006, 36(2): 107 -110 .
[7] JI Tao,GAO Xu/sup>,SUN Tong-jing,XUE Yong-duan/sup>,XU Bing-yin/sup> . Characteristic analysis of fault generated traveling waves in 10 Kv automatic blocking and continuous power transmission lines[J]. JOURNAL OF SHANDONG UNIVERSITY (ENGINEERING SCIENCE), 2006, 36(2): 111 -116 .
[8] . [J]. JOURNAL OF SHANDONG UNIVERSITY (ENGINEERING SCIENCE), 2009, 39(1): 27 -32 .
[9] WANG Li-ju,HUANG Qi-cheng,WANG Zhao-xu . [J]. JOURNAL OF SHANDONG UNIVERSITY (ENGINEERING SCIENCE), 2006, 36(6): 51 -56 .
[10] SUN Dianzhu, ZHU Changzhi, LI Yanrui. [J]. JOURNAL OF SHANDONG UNIVERSITY (ENGINEERING SCIENCE), 2009, 39(1): 84 -86 .