水生动物物种自动识别与检测新方法

IF 0.3
Pratik K.Agrawal, Vaishnavi Kamdi, Ishan Mittal, Pranav Bobde, Amarsingh Kashyap
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引用次数: 0

摘要

海洋渔业对任何国家的经济方面都作出了巨大贡献。印度拥有近8000公里的海岸线,在这里可以估计出渔业的剩余潜力。由于这片广阔的沿海地区,通过人工监测很难主动报告捕获的鱼类。在活跃期,计算机辅助方法是最合适的选择。本文重点研究了一种在单幅图像中识别单一和多个水生动物物种的方法。此外,还开发了响应式web和移动应用程序,其中集成了ML模型。这将帮助用户根据他们的使用情况访问数据。该方法使用轻量级目标检测算法YOLOv5n来检测这些物种。训练后的模型得到mAP@0.5:0.95的交集比联合(IoU),和平均精度(AP)为每个物种。该物种的AP也各不相同。YOLOv5n使用的gflop很少。这表明它是一个缩小版,能够在5.1 GFLOP树莓派3B+上运行。尽管使用的GFLOPs大大减少,但YOLOv5n的性能优于更快的R-CNN。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Novel Approach to Automatic Identification and Detection of Aquatic Animal Species
Marine fisheries contribute greatly to the economic aspects of any country. India, having a coastline of almost 8000 KM, a surplus of fisheries potential could be estimated here. Because of this vast coastal area, active reporting of captured fishes is difficult through manual monitoring. Computer-aided approach is the best suitable option during the active season. This paper focuses on investigating an approach for identifying single as well as multiple aquatic animal species in a single image. Further a responsive web as well as mobile application are developed, in which the ML models are integrated. This will help users to access data as per their use. The method used YOLOv5n, a lightweight object detection algorithm, to detect these species. The trained model yielded mAP@0.5:0.95 intersection over union (IoU), and average precision (AP) for each species. The species’ AP varied as well. Few GFLOPs are used by YOLOv5n. This indicates that it is a scaled-down version capable of running on the 5.1 GFLOP Raspberry Pi 3B+. Despite employing substantially fewer GFLOPs, YOLOv5n outperformed Faster R-CNN.
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来源期刊
International Journal of Next-Generation Computing
International Journal of Next-Generation Computing COMPUTER SCIENCE, THEORY & METHODS-
自引率
66.70%
发文量
60
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