大型智能视觉监控下的漏电监测软件设计方法

发布时间：2018-06-15 10:55

  本文选题：视觉监控 + 布尔核函数　； 参考：《电气应用》2014年22期

【摘要】：在长距离的电力运输中,对于漏电的监测效率较低。提出基于布尔核SVM识别算法的大型智能视觉监控下漏电监测方法。通过监控设备拍摄漏电发生时的电火花图像,对其图像进行颜色和形状参数特征的提取,构建布尔核SVM模型,完成监控设备拍摄下的漏电发生过程中电火花图像的基本识别。实验结果表明,利用该算法进行大型智能视觉监控下的漏电监测软件设计,能够极大地提高识别能力和识别的准确率,及时监测到漏电情况,保证了用电区域的安全。
[Abstract]:In long distance electric transportation, the efficiency of monitoring leakage is low. This paper presents a large scale intelligent visual monitoring method for leakage monitoring based on Boolean kernel SVM recognition algorithm. By using the monitoring equipment to capture the EDM image and extract the color and shape parameters of the EDM image, a Boolean kernel SVM model is constructed to recognize the EDM image in the process of the EDM generated by the monitoring equipment. The experimental results show that using this algorithm to design the leakage monitoring software under large-scale intelligent vision monitoring can greatly improve the recognition ability and the accuracy of recognition, monitor the leakage situation in time, and ensure the safety of the electric power area.
【作者单位】： 宁夏大学物理电气信息学院;
【分类号】：TM934.31;TP391.41
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本文编号：2021773