基于LightGBM模型的鱼类异常行为检测

针对传统理化方法分析水质污染情况耗时耗力等问题,提出一种基于鱼类异常行为识别的水质监测方法。以红色斑马鱼(red zebrafish)为研究对象,利用计算机视觉技术,首先对斑马鱼图像进行预处理,利用灰度共生矩阵获取鱼群纹理特征;然后通过Lucas-Kanade光流法计算鱼群的运动信息熵,并对获取的纹理特征和信息熵进行归一化处理;最后采用轻量化梯度促进机(LightGBM)对鱼类异常行为进行检测,与深度神经网络(DNN)和支持向量机(SVM)检测效果对比。结果显示:利用LightGBM对鱼类异常行为进行检测,准确率为98.5%,与其他模型对比分别提高0.5%和25.3%。研究表明,基于LightGBM模型的鱼类异常行为检测方法相比其他模型能更准确地识别鱼类非正常游动。该模型适用于自动水质监测。

Detection of fish abnormal behavior based on LightGBM model

Aiming at the problems of time-consuming and labor-consuming analysis of water pollution by traditional physical and chemical methods,a water quality monitoring method based on fish abnormal behavior recognition was proposed.In this paper,red zebrafish was used as the research object.Through computer vision technology,the zebrafish images were pre-processed first and GLCM was used to obtain the texture features of the fish school.Then Lucas-Kanade optical flow method was used to calculate the motion information entropy of fish,and the obtained texture features and information entropy were normalized.Finally,the LightGBM was used to detect the abnormal behaviors of fish for comparison with the detection results of DNN and SVM.The results showed that the accuracy rate of the fish abnormal behavior detection with LightGBM was 98.5%,which was improved by 0.5%and 25.3%respectively compared with other models.Researches show that the LightGBM model-based fish abnormal behavior detection method can more accurately identify abnormal fish swimming than other models,and is suitable for automatic water quality monitoring.