Segmentation and density statistics of mariculture cages from remote sensing images using mask R-CNN  

作　　者：Chuang Yu Zhuhua Hu Ruoqing Li Xin Xia Yaochi Zhao Xiang Fan Yong Bai 

机构地区：[1]School of Information and Communication Engineering,School of Computer Science&Cyberspace Security,Hainan University,No.58,Renmin Avenue,Haikou 570228,PR China [2]Shenyang Institute of Automation,Chinese Academy of Sciences,Shenyang 110016,PR China

出　　处：《Information Processing in Agriculture》2022年第3期417-430,共14页农业信息处理（英文）

基　　金：This research was supported by the National Natural Science Foundation of China(Grant No.61963012);the Hainan Provincial Natural Science Foundation of China(Grant No.620RC564,Grant No.619QN195).The authors would like to thank the referees for their constructive suggestions.

摘　　要：The normal growth of fishes is closely relevant to the density of mariculture. It is of greatsignificance to accurately calculate the breeding area of specific sea area from satelliteremote sensing images. However, there are no reports about cage segmentation and den-sity detection based on remote sensing images so far. And the accurate segmentation ofcages faces challenges from very large high-resolution images. Firstly, a new public mari-culture cage data set is built. Secondly, the training set is augmented via sample variationsto 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 tech-nologies, the entire remote sensing test images of the cage can be accurately segmented.Finally, using the trained model, the object detection features and segmentation character-istics can be obtained at the same time. Considering only the area within the target detec-tion frame, the proposed method can count the pixels in the segmented area, which canobtain accurate area and density while reducing time-consuming. Experimental resultsdemonstrate that, compared with traditional contour extraction method and U-Net basedscheme, the proposed scheme can significantly improve segmentation precision and mod-el’s robustness. The relative error of the actual area is only 1.3%.

关 键 词：Deep learning Mask R-CNN Image segmentation Remote sensing 

分 类 号：S95[农业科学—水产养殖]