上海海洋大学学报2025，Vol.34Issue(2)：307-319,13.DOI:10.12024/jsou.20240604587
基于深度学习的金枪鱼延绳钓渔获图像识别技术分析
Analysis of deep learning-based tuna longline catch image recognition technique
摘要
Abstract
In order to achieve efficient identification and classification of tuna longline catches and to improve the accuracy of fishery resources monitoring,this study explores a fish image recognition method based on convolutional neural network.The experiments were conducted using image data of three economic fish species and ten bycatch species caught by the Songhang of Shanghai Ocean University during the high seas survey in the western and central Pacific Ocean,and a convolutional neural network(CNN)based on a single shot multiBox detector(SSD)was used to classify and recognise the images.The training dataset is optimised by comparing and analysing the local fish images with the overall image dataset to improve the classification performance of the model.The experimental results show that the classification accuracy of the improved fish image dataset on the SSD model reaches 91.6%,which is a 6.2%improvement compared to the original dataset.The study shows that using the optimised dataset,the SSD model can significantly improve the recognition accuracy of tuna longline catches with better stability and adaptability.This study provides an effective technical path for fishery resource monitoring based on convolutional neural networks,especially in improving the automatic classification and identification accuracy of tuna longline catches,which is of great significance for promoting sustainable fishery management and marine ecological protection.关键词
金枪鱼延绳钓/渔获物识别/卷积神经网络/数据集优化/中西太平洋Key words
tuna longlining/catch identification/convolutional neural networks/dataset optimisation/Western and Central Pacific Ocean分类
水产学引用本文复制引用
夏超,陈新军,刘必林,孔祥洪,叶旭昌..基于深度学习的金枪鱼延绳钓渔获图像识别技术分析[J].上海海洋大学学报,2025,34(2):307-319,13.基金项目
上海市高校特聘教授"东方学者"岗位跟踪计划项目(GZ2022011) （GZ2022011）
农业农村部全球渔业资源调查监测评估(公海渔业资源综合科学调查)专项(D-8025-23-1002) （公海渔业资源综合科学调查）
