| 引用本文: | 刘辉,王亚楠,陈棕鑫.自适应变步长高斯混合模型的工业烟尘目标分割方法[J].控制理论与应用,2020,37(11):2441~2450.[点击复制] |
| LIU Hui,WANG Ya-nan,CHEN Zong-xin.Method to segment industrial emission smoke target based on adaptive variable step Gauss mixture model[J].Control Theory and Technology,2020,37(11):2441~2450.[点击复制] |
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| 自适应变步长高斯混合模型的工业烟尘目标分割方法 |
| Method to segment industrial emission smoke target based on adaptive variable step Gauss mixture model |
| 摘要点击 1758 全文点击 512 投稿时间：2019-11-26 修订日期：2020-06-13 |
| 查看全文 查看/发表评论 下载PDF阅读器 |
| DOI编号 10.7641/CTA.2020.90977 |
| 2020,37(11):2441-2450 |
| 中文关键词 图像分割 目标检测 背景减除 背景建模 高斯混合模型 烟尘分割 |
| 英文关键词 image segmentation target detection gauss mixture model background modeling smoke segmentation background subtraction |
| 基金项目 国家自然科学基金项目(61863018)资助. |
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| 中文摘要 |
| 工业烟尘排放时的烟气黑度自动监测对提高环保质量和指导生产过程具有重要的应用价值, 针对传统的
高斯混合模型在进行背景建模时, 参数是在固定帧值的基础上进行参数更新, 导致烟尘检测不准确等问题, 提出一
种自适应变步长高斯混合模型的工业烟尘图像分割方法. 根据烟尘变化速度不均匀的特点, 通过分析检测出烟尘
与实际烟尘的检出率和检准率的和的最大值, 计算熵值差变化率对应的最佳步长, 得到熵值差变化率与最佳步长的
模型. 以熵值差变化率为依据, 确定最佳步长, 得到一个关于熵值差变化率与最佳步长的模型. 以熵值差变化率为
输入, 以最佳步长为输出, 在广义回归神经网络(GRNN)得到适用于本文工业烟尘图像分割的网络模型. 最后, 在多
个场景的烟尘视频中进行分割实验, 结果表明, 本文中方法能够有效的分割出视频中烟尘区域, 且具有一定的适用
性. |
| 英文摘要 |
| Automatic monitoring of smoke blackness during industrial smoke emission has important application value
for improving environmental protection quality and guiding production process. Aiming at the problem of inaccurate smoke
detection caused by updating parameters on the basis of previous fixed frame values in traditional Gauss mixture model
for background modeling. A method to segment industrial emission smoke based on adaptive variable step is proposed.
Combining the characteristics of uneven change speed of smoke, the maximum sum of detection rate and detection accuracy
rate of smoke and dust is analyzed, and the optimal step length corresponding to the change rate of entropy difference is
calculated, and the model of the change rate of entropy difference and the optimal step length is obtained. Based on
the change rate of entropy difference, the optimum step size is determined, and a model about the optimum step size is
obtained. With the change rate of entropy difference as input and the optimal step size as output, a network model is
obtained in generalized regression neural network network. Finally, segmentation experiments are carried out in smoke
several videos. The results shows the effectiveness of the proposed method. |
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