• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文会议>International Joint Conference on Neural Networks >Assessing fish abundance from underwater video using deep neural networks
【24h】

Assessing fish abundance from underwater video using deep neural networks

机译：使用深度神经网络从水下视频评估鱼的丰度

获取原文
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

Uses of underwater videos to assess diversity and abundance of fish are being rapidly adopted by marine biologists. Manual processing of videos for quantification by human analysts is time and labour intensive. Automatic processing of videos can be employed to achieve the objectives in a cost and time-efficient way. The aim is to build an accurate and reliable fish detection and recognition system, which is important for an autonomous robotic platform. However, there are many challenges involved in this task (e.g. complex background, deformation, low resolution and light propagation). Recent advancement in the deep neural network has led to the development of object detection and recognition in real time scenarios. An end-to-end deep learningbased architecture is introduced which outperformed the state of the art methods and first of its kind on fish assessment task. A Region Proposal Network (RPN) introduced by an object detector termed as Faster R-CNN was combined with three classification networks for detection and recognition of fish species obtained from Remote Underwater Video Stations (RUVS). An accuracy of 82.4% (mAP) obtained from the experiments are much higher than previously proposed methods.
机译：海洋生物学家正在迅速采用水下录像来评估鱼类的多样性和丰富度。由人工分析人员手动处理视频以进行量化是费时费力的。可以采用视频的自动处理以节省成本和时间的方式实现目标。目的是建立一个准确而可靠的鱼类检测和识别系统，这对于自主机器人平台非常重要。但是，此任务涉及许多挑战（例如，复杂的背景，变形，低分辨率和光传播）。深度神经网络的最新进展已导致实时场景中对象检测和识别的发展。介绍了一种基于端到端深度学习的体系结构，该体系结构优于最新方法，并且在鱼类评估任务方面是首屈一指的。由对象检测器引入的区域提议网络（RPN）（称为Faster R-CNN）与三个分类网络结合在一起，用于检测和识别从远程水下视频站（RUVS）获得的鱼类。从实验中获得的82.4％（mAP）的准确性比以前提出的方法要高得多。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号