基于斑马鱼幼鱼行为轨迹的水质检测模型研究

Research on Water Quality Detection Model Based on the Behavior Trajectory of Zebrafish Juveniles

摘要: 水污染对人类的生命和财产安全构成了严重威胁，因此建立有效的水质检测模型成为水质管理中至关重要的环节。目前，常用的水质检测方法主要包括理化检测法和生物检测法，其中生物检测法可以弥补理化检测法的不足，能够更加准确和高效地反映水质状况。本文基于计算机视觉原理，选择斑马鱼幼鱼作为水质指示生物，通过获取斑马鱼幼鱼在正常水质、轻度污染水质和重度污染水质下的游动视频，运用目标检测与轨迹跟踪技术生成鱼体运动的轨迹图像作为数据集，然后采用机器学习和图像分类技术，对不同水质下的鱼类运动轨迹特征进行学习和分类，建立了一种基于斑马鱼幼鱼行为跟踪轨迹的生物水质检测模型。该模型的分类准确率达到了97.4%，对随机轨迹图像的分类预测准确率达到了98.6%以上。本研究不仅为生物水质检测提供了模型设计的理论依据，也为不同应用场景提供了技术上的可行性支持。

Abstract: Water pollution poses a serious threat to human life and property safety, so establishing an effective water quality detection mechanism has become a crucial link in water quality management. At present, the commonly used water quality detection methods mainly include physical and chemical detection methods and biological detection methods. Among them, biological detection methods can make up for the shortcomings of physical and chemical detection methods and can reflect water quality conditions more accurately and efficiently. Based on the principle of computer vision, this paper selects zebrafish larvae as water quality indicator organisms. By obtaining the swimming videos of zebrafish larvae in normal water quality, lightly polluted water quality and heavily polluted water quality, the target detection and trajectory tracking technology are used to generate the trajectory images of fish body movement as the data set. Then, machine learning and image classification technology are used to learn and classify the characteristics of fish movement trajectories under different water qualities, and a biological water quality detection model based on zebrafish larvae behavior tracking trajectory is established. The classification accuracy of the model reached 97.4%, and the classification prediction accuracy of random trajectory images reached more than 98.6%. This study not only provides a theoretical basis for model design for biological water quality detection, but also provides technical feasibility support for different application scenarios.