Length estimation of fish detected as non-occluded using a smartphone application and deep learning method

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外文摘要:Uncertainty in stock assessment can be reduced if accurate and precise length composition of catch is available. Length data are usually manually collected, although this method is costly and time-consuming. Recently, some studies have estimated fish species and length from images using deep learning by installing camera systems in fishing vessels or a fish auction center (' Alvarez -Ellacuria et al., 2020; Lekunberri et al., 2022; Ovalle et al., 2022; Palmer et al., 2022). Once a deep learning model is properly trained, it does not require expensive and timeconsuming manual labor. However, several studies on the deep learning models had monitoring fishing practices using electronic monitoring systems; therefore, it is necessary to solve many issues, such as counting the total number of fish in the catch. In this study, we proposed a new deep learning-based method to estimate fish length using images. Species identification was not performed by the model, and images were taken manually by the measurers; however, length composition was obtained only for non-occluded fish detected by the model. A smartphone application was developed to calculate scale information (cm/pixel) from a known size fish box in fish images, and the Mask R-CNN (Region-based convolutional neural networks) model was trained using 76,161 fish to predict non-occluded fish. Two experiments were conducted to confirm whether the proposed method resulted in errors in the length composition. First, we manually measured the total length (TL) for four species and one genus (categories), estimated the TL using a deep learning method, and calculated the bias. Second, multiple fish in a fish box were photographed simultaneously, and the relative difference between the mean TL estimated from the non-occluded fish and the true mean TL from all fish was calculated. The results showed that the biases of all five categories were from -0.69 cm to 0.37 cm and the range of difference was from -1.14 % to 1.40 % regardless of the number of fish in the fish box. The deep learning method was used not to replace the measurer but to increase their measurement efficiency. The proposed method is expected to increase opportunities for the application of deep learning-based fish length estimation in areas of research that are different from the scope of conventional electronic monitoring systems.
外文关键词:Mask R-CNN;stock assessment;occlusion;Mobile imaging;Total length composition in catch
作者:Shibata, Yasutoki;Iwahara, Yuka;Manano, Masahiro;Kanaya, Ayumi;Sone, Ryota;Tamura, Satoko;Kakuta, Naoya;Nishino, Tomoya;Ishihara, Akira;Kugai, Shungo
作者单位:Japan Fisheries Res & Educ Agcy;Fisheries Resources Inst;Aichi Fisheries Res Inst;Fisheries Technol Ctr Sagami Bay Expt Stn Kanagawa;Computermind Corp
期刊名称:FISHERIES RESEARCH
期刊影响因子:0.0
出版年份:2024
出版刊次:273
原文传递申请:江苏省科技资源(工程技术文献)统筹服务平台

  1. 编译服务:智慧农业
  2. 编译者:虞德容
  3. 编译时间:2025-04-03