基于多探测器和运动预测的高速非侵入式鱼苗计数法

IF 4.4 1区 农林科学 Q1 AGRICULTURAL ENGINEERING
Jialong Zhou , Zhangying Ye , Jian Zhao , Daxiong Ji , Zequn Peng , Guoxing Lu , Musa Abubakar Tadda , Abubakar Shitu , Songming Zhu
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引用次数: 0

摘要

鱼苗计数是鱼类养殖的一项基本操作,为水产养殖的许多方面提供了重要指导。然而,目前大多数计数方法效率低下或计算繁琐。本研究提出了一种基于多检测器和运动预测模型的高速、非侵入式鱼苗计数方法,在低帧率条件下实现了高精度计数。首先,为了有效地检测和定位附着的幼指,检测器由基于高斯混合(MOG)的分割算法和基于局部极值的 Blob 检测算法组合完成。其次,使用三种不同的函数构建运动预测模型,以预测小指在前一帧中每个位置的大致概率。第三,以概率为特征构建代价矩阵,关联连续帧中的小指头,并对新出现的小指头进行实时计数,实现了高精度的连续小指头计数。在低帧频(10 帧/秒)采集条件下,以大口鲈鱼(Micropterus salmoides,3-5 厘米)和鲫鱼(Carassius auratus,2-6 厘米)为对象,对采集到的 52 个数据集进行了测试和分析,结果表明带分割功能的最佳运动预测模型对两种鱼类的平均计数准确率超过 99%,准确率的标准偏差小于 0.7%。该方法为基于计算机视觉的鱼苗计数提供了一种低成本、高速、稳定的应用解决方案。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Multi-detector and motion prediction-based high-speed non-intrusive fingerling counting method

Fingerling counting is a basic operation in fish farming and provides an important guideline for many aspects of aquaculture. However, most of the current counting methods are inefficient or computationally cumbersome. This study proposed a high-speed, non-intrusive fingerling counting method based on multiple detectors and a motion prediction model, which achieved high-accuracy counting under the condition of low-frame rate. Firstly, to effectively detect and locate the adherent fingerlings, the detector was accomplished by combining the mixture of Gaussian-based (MOG) segmentation algorithm and the local extremum-based blob detection algorithm. Secondly, three different functions were used to construct a motion prediction model to predict the approximate probability of the fingerlings at each position in the previous frame. Thirdly, the cost matrix was constructed with probability as the feature to associate the fingerlings in the consecutive frames, and the newly appeared fingerlings were counted in real-time, realising the continuous fingerling counting with high precision. Through testing and analysis on 52 collected datasets under low-frame-rate (10 fps) acquisition conditions using largemouth bass (Micropterus salmoides, 3–5 cm) and crucian carp (Carassius auratus, 2–6 cm), results indicated that the best motion prediction model with segmentation function reached over 99% average counting accuracy for both species, with a standard deviation of accuracy less than 0.7%. This method provides a low-cost, high-speed, and stable application solution for computer vision-based fingerling counting.

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来源期刊
Biosystems Engineering
Biosystems Engineering 农林科学-农业工程
CiteScore
10.60
自引率
7.80%
发文量
239
审稿时长
53 days
期刊介绍: Biosystems Engineering publishes research in engineering and the physical sciences that represent advances in understanding or modelling of the performance of biological systems for sustainable developments in land use and the environment, agriculture and amenity, bioproduction processes and the food chain. The subject matter of the journal reflects the wide range and interdisciplinary nature of research in engineering for biological systems.
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