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| 基于粒子群优化算法的水源微生物自动识别 |
| Automatic recognition of water source microorganisms based on particle swarm optimization algorithm |
| 投稿时间：2022-11-18 |
| DOI：10.3969/j.issn.1005-5630.2023.002.002 |
| 中文关键词: 微生物识别 图像分割 粒子群算法 支持向量机 |
| 英文关键词:microorganisms recognition image segmentation particle swarm algorithm support vector machine |
| 基金项目:国家重点研发计划专项（2020YFF01014503） |
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| 摘要点击次数: 1440 |
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| 中文摘要: |
| 水源微生物检测在水源生物安全监测等方面具有非常重要的意义，而传统的显微镜观测等方法存在效率低、需要专业人员操作等不足，为此提出了一种水源微生物自动识别方法。采集水样，并制作水源微生物图像集，编写全自动与半自动两种图像分割算法用于提取目标微生物区域，并提取6种图像特征。基于以上特征数据，研究水源微生物识别模型的优化问题：首先，优化部分特征参数；接着，融合所有特征，建立粒子群优化算法的支持向量机（support vector machine optimized by particle swarm optimization, PSO-SVM）微生物识别模型，并与其他识别算法进行比较。结果表明，相比于其他3种算法，PSO-SVM能更有效地识别各种微生物，其平均识别率达到97.08%。 |
| 英文摘要: |
| The detection of water source microorganisms is of great significance to the biosafety of water source and so on. However, the traditional methods such as microscopic observation are inefficient and need professional personnel. Therefore, an automatic recognition method of micro-organisms in water source is proposed. Water samples were collected and a microorganisms image set was made. Automatic and semi-automatic image segmentation algorithms were proposed to extract the target microorganisms area, and 6 features were extracted. The model optimization problem of water microorganisms classification process was studied. First, the parameters of a few features were optimized. Then, all the features were fused, and a microorganisms recognition model of support vector machine optimized by particle swarm optimization (PSO-SVM) was established and compared with other recognition algorithms. The results show that, compared with the other 3 recognition algorithms, PSO-SVM can recognize different kinds of microorganisms more effectively, with an average recognition rate of 97.08%. |
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