Distillation method based on global background strengthen for Enteromorpha prolifera target detection

doi:10.13474/j.cnki.11-2246.2024.0604

Abstract

Abstract: The detection of Enteromorpha prolifera stands as a pivotal research area within the realm of intelligent marine environment monitoring.Addressing the challenge posed by the substantial training sample requirements inherent in conventional methods of Enteromorpha prolifera detection,this paper proposes GBS-LD model.By introducing a global context module and background distillation loss branch,the shortcomings of the original position distillation method in modeling background features are solved,effectively improving the robustness of detection system in complex marine environments.Our proposed model has achieved high accuracy and real-time performance in the dataset of Enteromorpha prolifera,providing important reference for intelligent monitoring of marine.

Key words: Enteromorpha prolifera, localization distillation, global background strengthing, object detection, deep leaning

CLC Number:

P237

LIU Bing, LIU Yu, JIN Fengxue, ZOU Yibo, GE Yan, ZHAO Linlin. Distillation method based on global background strengthen for Enteromorpha prolifera target detection[J]. Bulletin of Surveying and Mapping, 2024, 0(6): 19-23.

References

[1] 王怡人,王胜强,喻樾,等.一种提取南黄海浒苔的自适应阈值遥感算法[J].遥感信息,2021,36(2):120-129.
[2] 麻德明,张秀林,田梓文,等.无人机遥感影像浒苔信息提取方法[J].测绘通报,2023(8):24-28.
[3] WANG Shengke,LIU Lu,QU Liang,et al.Accurate Ulva prolifera regions extraction of UAV images with superpixel and CNNs for ocean environment monitoring[J].Neurocomputing,2019,348(C):158-168.
[4] GAO Le,LI Xiaofeng,KONG Fanzhou,et al.AlgaeNet:a deep-learning framework to detect floating green algae from optical and SAR imagery[J].IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing,2022,15:2782-2796.
[5] JIN Xifang,LI Yun,WAN Jianhua,et al.MODIS green-tide detection with a squeeze and excitation oriented generative adversarial network[J].IEEE Access,2022,10:60294-60305.
[6] HINTON G E,VINYALS O,DEAN J.Distilling the knowledge in a neural network[EB/OL].[2015-03-09].https://arxiv.org/abs/1503.02531.
[7] ZHENG Zhaohui,YE Rongguang,WANG Ping,et al.Localization distillation for dense object detection[C]//Proceedings of 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).New Orleans:IEEE,2022:9397-9406.
[8] LI X,WANG W,WU L,et al.Generalized focal loss:learning qualified and distributed bounding boxes for dense object detection[J].Advances in Neural Information Processing Systems,2020,33:21002-21012.
[9] TIAN Zhi,SHEN Chunhua,CHEN Hao,et al.FCOS:fully convolutional one-stage object detection[C]//Proceedings of 2019 IEEE/CVF International Conference on Computer Vision (ICCV).Seoul:IEEE,2019:9626-9635.
[10] GUO Jianyuan,HAN Kai,WANG Yunhe,et al.Distilling object detectors via decoupled features[C]//Proceedings of 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).Nashville:IEEE,2021:2154-2164.
[11] YANG Zhendong,LI Zhe,JIANG Xiaohu,et al.Focal and global knowledge distillation for detectors[C]//Proceedings of 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).New Orleans:IEEE,2022:4633-4642.
[12] HE Kaiming,ZHANG Xiangyu,REN Shaoqing,et al.Deep residual learning for image recognition[C]//Proceedings of 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).Las Vegas:IEEE,2016:770-778.
[13] CHEN Guobin,CHOI W,YU Xiang,et al.Learning efficient object detection models with knowledge distillation[C]//Proceedings of the 31st International Conference on Neural Information Processing Systems.Long Beach:ACM Press, 2017:742-751.
[14] SUN Ruoyu,TANG Fuhui,ZHANG Xiaopeng,et al.Distilling object detectors with task adaptive regularization[EB/OL].[2020-6-23].https://arxiv.org/abs/2006.13108.
[15] WANG Tao,YUAN Li,ZHANG Xiaopeng,et al.Distilling object detectors with fine-grained feature imitation[C]//Proceedings of 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).Long Beach:IEEE,2019:4928-4937.
[16] CARION N,MASSA F,SYNNAEVE G,et al.End-to-end object detection with transformers[C]//Proceedings of 2020 European Conference on Computer Vision.Cham:Springer,2020:213-229.