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解卷增强关键点网络,以实现有效的鱼计数

Ximing Li¹, Zhicai Liang¹, Yitao Zhuang¹

¹College of Mathematics and Informatics, South China Agricultural University, Guangzhou 510642, China.

Animals : an open access journal from MDPI

|May 25, 2024

概括

一种新的方法,即解卷增强关键点网络 (DEKNet),使用单个关键点方法准确地计算鱼. 这种先进的幼鱼计数技术实现了高精度,提高了水产养殖的效率.

科学领域:

计算机视觉计算机视觉
水产养殖技术水产养殖技术
机器学习机器学习

背景情况:

准确的鱼计数对于养殖鱼类至关重要,但由于遮,密度和小尺寸而具有挑战性.
现有的基于计算机的方法在大规模的 enumeration 中难以提高效率和准确性.

研究的目的:

开发一种准确和有效的方法来计数鱼,使用一种新的单一关键点方法.
引入解卷增强关键点网络 (DEKNet) 以改进鱼数目.

主要方法:

DEKNet采用鱼特征提取器 (FFE) 具有平行双分支用于高分辨率表示.
两个相同的解卷模块 (TDM) 生成一个高分辨率的关键点热图.
鱼是通过热图中的局部峰值来识别的,使得精确的计数和定位成为可能.

主要成果:

DEKNet在FishFry-2023数据集上计算鱼的准确率达到了98.59%.
该方法在Penaeus数据集上显示出高精度 (98.51%),在脂肪细胞细胞上显示出低MAE (13.32).
与现有方法相比,DEKNet在准确性,参数数量和计算力方面表现出卓越的性能.

结论:

在具有挑战性的条件下,DEKNet提供了一种强大而有效的解决方案,用于准确计数幼鱼.
单个关键点方法和解卷增强显著提高计数精度.
这项研究推进了水产养殖和相关生物应用中的自动计数技术.

关键词:

解体解体是一种解体.鱼子数量计算鱼子数量计算热图是一种热图.一个关键的关键点.

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