上海海洋大学学报2026，Vol.35Issue(5)：1242-1255,14.DOI:10.12024/jsou.20250604879
基于自注意力与CNN-LSTM的西北太平洋四类典型渔船捕捞状态识别
Fishing state recognition for four typical vessels in the Northwest Pacific Ocean based on self-attention and CNN-LSTM
摘要
Abstract
To efficiently extract fishing status from automatic identification system(AIS)data and provide critical technical support for combating illegal,unreported,and unregulated(IUU)fishing,this study proposes a hybrid deep learning model that integrates a multi-scale convolutional neural network(CNN)and a long short-term memory(LSTM)network,enhanced with a multi-head self-attention(MHSA)mechanism.The model takes AIS trajectory segments from four typical vessel types(longliners,purse seiners,trawlers,and squid jiggers)in the Northwest Pacific Ocean as input,comprehensively extracting features such as vessel speed,heading,spatiotemporal location,and their dynamic changes.To address the issue of class imbalance,a class weighting mechanism was incorporated during model training.The experimental results,based on 2020 AIS data,show that the model achieves recognition accuracies of 85.4%for non-fishing and 84.8%for fishing states,demonstrating good stability and discrimination ability.The model exhibits good recognition performance for trawlers,purse seiners,and squid jiggers,with accuracies exceeding 88%.However,the recall rate for longliners is relatively low(73.12%),indicating that their complex operational behavior poses a challenge for the model.The analysis also reveals that different vessel types exhibit significant variations in fishing speed distribution,diurnal patterns,and seasonal spatial distribution.The developed model effectively identifies the fishing status of multiple vessel types,compensating for the limitations of traditional rule-based or clustering methods in complex behavior recognition.This research can provide technical support for improving dynamic monitoring and regulatory efficiency,enabling IUU fishing behavior detection,and supporting fishery resource assessment.关键词
自动识别系统(AIS)/捕捞状态识别/卷积神经网络(CNN)/长短期记忆网络(LSTM)/时空特征分析Key words
automatic identification system(AIS)/fishing state recognition/convolutional neural network(CNN)/long short-term memory(LSTM)/spatiotemporal feature analysis分类
农业科技引用本文复制引用
高雅,常亮,陈新军,吴峰,杨承昊,黄博,彭愉溪,胡甚平..基于自注意力与CNN-LSTM的西北太平洋四类典型渔船捕捞状态识别[J].上海海洋大学学报,2026,35(5):1242-1255,14.基金项目
国家重点研发计划(2024YFD2400603) （2024YFD2400603）
国家自然科学基金(42476086) （42476086）
大洋渔业资源可持续开发教育部重点实验室开放基金(A1-2006-25-200203) （A1-2006-25-200203）
