基于视频的延绳钓渔获物分级监测
Video-based Hierarchical Species Classification for Longline Fishing Monitoring
摘要：
延绳钓捕捞电子监测的目的是监测渔船的捕鱼活动，无论是为了遵守法规还是为了计算渔获量。基于视频的层次分类允许廉价和有效的鱼种识别的渔获物从延绳钓捕捞，鱼类在捕捞过程中受到严重的变形和自我封闭。更重要的是，分层分类的灵活性通过提供不同层次的置信度得分来减轻人工审查的费力工作。一些相关的工作或者使用级联模型进行分层分类，或者对每个图像进行预测，或者预先预测数据集的一个重叠的分层数据结构。然而，与渔业科学家提供的一个已知的非重叠的分层数据结构，我们的方法强制执行的分层数据结构，并介绍了一个有效的培训和推理策略，基于视频的渔业数据。我们的实验表明，所提出的方法优于经典的平面分类系统显着和我们的消融研究证明我们的贡献在CNN模型设计，训练策略，和基于视频的推理方案的分层鱼类物种分类任务。
英文摘要：
The goal of electronic monitoring (EM) of longline fishing is to monitor the fish catching activities on fishing vessels, either for the regulatory compliance or catch counting. Hierarchical classification based on videos allows for inexpensive and efficient fish species identification of catches from longline fishing, where fishes are under severe deformation and self-occlusion during the catching process. More importantly, the flexibility of hierarchical classification mitigates the laborious efforts of human reviews by providing confidence scores in different hierarchical levels. Some related works either use cascaded models for hierarchical classification or make predictions per image or predict one overlapping hierarchical data structure of the dataset in advance. However, with a known non-overlapping hierarchical data structure provided by fisheries scientists, our method enforces the hierarchical data structure and introduces an efficient training and inference strategy for video-based fisheries data. Our experiments show that the proposed method outperforms the classic flat classification system significantly and our ablation study justifies our contributions in CNN model design, training strategy, and the video-based inference schemes for the hierarchical fish species classification task.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
