基于计算机视觉与SVM的水质异常监测方法

Anomaly Monitoring Method of Water Quality Based on Computer Vision and Support Vector Machine

针对水质异常监测问题,本文提出了一种基于计算机视觉技术和支持向量机相结合的生物式水质异常监测方法。首先通过计算机视觉获取可以反映水质状况的鱼类行为运动特征参数,对其进行预处理;然后建立样本集并获得基于SVM的水质异常监测模型;最后利用模型对未知水质下的鱼类行为特征参数分析评价,间接监测水质异常状况。鉴于支持向量机核函数类型和参数优化对模型优劣有重大影响,本文对不同类型的核函数进行实验对比,其次分别采用粒子群优化算法(PSO)、遗传算法(GA)以及网格搜索法(Grid Search)对参数进行优化选择。实验结果表明该方法可以快速有效的进行水质异常监测。

Aiming at the problem of water quality anomaly monitoring, a bio-monitoring method based on computer vision and support vector machine is proposed. First, fish behavior movement information is collected by computer vision. Then, establishing training sample set is used for obtaining water quality anomaly monitoring model. Finally, the model is utilized to analyze the fish data of unknown water quality. Kernel function type and parameter optimization have a significant impact on the model. The different types of kernel function experimental results are compared to choose the best kernel, and then Particle Swarm Optimization（PSO） algorithm and Genetic Algorithm（GA） and the Grid Search method（Grid Search） are used to optimize parameter. The experimental results show that the method can monitor the water quality quickly and efficiently.