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Segmentation and density statistics of mariculture cages from remote sensing images using mask R-CNN
Yu, Chuang1,2; Hu ZH(胡祝华)1; Li, Ruoqing1; Xia, Xin1; Zhao YC(赵瑶池)1; Fan, Xiang1; Bai Y(白勇)1
Department其他
Source PublicationInformation Processing in Agriculture
ISSN2214-3173
2021
Pages1-14
Indexed ByEI
EI Accession number20212010363991
Contribution Rank1
Funding OrganizationNational Natural Science Foundation of China (Grant No. 61963012) ; Hainan Provincial Natural Science Foundation of China (Grant No. 620RC564, Grant No. 619QN195)
KeywordDeep learning Mask R-CNN Image segmentation Remote sensing
Abstract

The normal growth of fishes is closely relevant to the density of mariculture. It is of great significance to accurately calculate the breeding area of specific sea area from satellite remote sensing images. However, there are no reports about cage segmentation and density detection based on remote sensing images so far. And the accurate segmentation of cages faces challenges from very large high-resolution images. Firstly, a new public mariculture cage data set is built. Secondly, the training set is augmented via sample variations to improve the robustness of the model. Then, for cage segmentation and density statistics, a new methodology based on Mask R-CNN is proposed. Using dividing and stitching technologies, the entire remote sensing test images of the cage can be accurately segmented. Finally, using the trained model, the object detection features and segmentation characteristics can be obtained at the same time. Considering only the area within the target detection frame, the proposed method can count the pixels in the segmented area, which can obtain accurate area and density while reducing time-consuming. Experimental results demonstrate that, compared with traditional contour extraction method and U-Net based scheme, the proposed scheme can significantly improve segmentation precision and model's robustness. The relative error of the actual area is only 1.3%.

Language英语
Document Type期刊论文
Identifierhttp://ir.sia.cn/handle/173321/28906
Collection其他
Corresponding AuthorHu ZH(胡祝华); Zhao YC(赵瑶池)
Affiliation1.School of Information and Communication Engineering, School of Computer Science & Cyberspace Security, Hainan University, No. 58, Renmin Avenue, Haikou 570228, China
2.Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, China
Recommended Citation
GB/T 7714
Yu, Chuang,Hu ZH,Li, Ruoqing,et al. Segmentation and density statistics of mariculture cages from remote sensing images using mask R-CNN[J]. Information Processing in Agriculture,2021:1-14.
APA Yu, Chuang.,Hu ZH.,Li, Ruoqing.,Xia, Xin.,Zhao YC.,...&Bai Y.(2021).Segmentation and density statistics of mariculture cages from remote sensing images using mask R-CNN.Information Processing in Agriculture,1-14.
MLA Yu, Chuang,et al."Segmentation and density statistics of mariculture cages from remote sensing images using mask R-CNN".Information Processing in Agriculture (2021):1-14.
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