https://scholars.lib.ntu.edu.tw/handle/123456789/549047
標題: | Detecting and counting harvested fish and identifying fish types in electronic monitoring system videos using deep convolutional neural networks | 作者: | Tseng, C.-H. YAN-FU KUO |
關鍵字: | Convolutional neural networks; Fish body length; Fish type identification; Instance segmentation; Resource management | 公開日期: | 2020 | 卷: | 77 | 期: | 4 | 起(迄)頁: | 1367-1378 | 來源出版物: | ICES Journal of Marine Science | 摘要: | The statistics of harvested fish are key indicators for marine resource management and sustainability. Electronic monitoring systems (EMSs) are used to record the fishing practices of vessels in recent years. The statistics of the harvested fish in the EMS videos are manually read and recorded later by operators in data centres. However, this manual recording is time consuming and labour intensive. This study proposed an automatic approach for prescreening harvested fish in the EMS videos using convolutional neural networks (CNNs). In this study, harvested fish in the frames of the EMS videos were detected and segmented from the background at the pixel level using mask regional-based CNN (mask R-CNN). The number of the fish was determined using time thresholding and distance thresholding methods. Subsequently, the types and body lengths of the fish were determined using the confidence scores and the masks predicted by the mask R-CNN model, respectively. The trained mask R-CNN model attained a recall of 97.58% and a mean average precision of 93.51% in terms of fish detection. The proposed method for fish counting attained a recall of 93.84% and a precision of 77.31%. An overall accuracy of 98.06% was obtained for fish type identification. © International Council for the Exploration of the Sea 2020. All rights reserved. |
URI: | https://www.scopus.com/inward/record.url?eid=2-s2.0-85091626308&partnerID=40&md5=22faf159e38a1408276d4f2bf46d1361 https://scholars.lib.ntu.edu.tw/handle/123456789/549047 |
DOI: | 10.1093/icesjms/fsaa076 | SDG/關鍵字: | artificial neural network; detection method; fish; harvesting; marine resource; monitoring system; precision; resource management |
顯示於: | 生物機電工程學系 |
在 IR 系統中的文件,除了特別指名其著作權條款之外,均受到著作權保護,並且保留所有的權利。