PCNN Infrared Fault Region Detection Along Transmission Lines Based on the MST Framework
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摘要: 针对输电线路电气设备红外热故障检测，提出采用一种基于最大相似度阈值（Maximum Similarity Thresholding, MST）的脉冲耦合神经网络（Pulse-coupled neural Network, PCNN）红外图像热故障区域提取方法。在该方法中，利用脉冲耦合神经元对相似的邻域神经元同步点火特性，通过引入最大相似度阈值框架，简化了PCNN模型的阈值设置机制。同时，针对相似邻域神经元的同步点火特性，采用最小聚类方差设置连接系数，使得PCNN模型在自适应迭代下最终获取热故障区域。最后通过真实输电线路电气设备红外故障图像测试，验证了文中所提方法的有效性和适用性，为PCNN模型的推广应用奠定了基础。Abstract: This paper presents a pulse-coupled neural network (PCNN) method for infrared fault region extraction based on maximum similarity thresholding to detect the fault region from the infrared image of a transmission line. In this method, the synchronous pulse characteristics of the PCNN model are used to cluster pixels via inner iteration, and the model is simplified by incorporating the maximum similarity thresholding method, enabling the PCNN model to simplify the thresholding setting. Meanwhile, the minimum clustering variance is introduced to set the linking coefficient. Thus, the PCNN model can efficiently segment an infrared image and obtain the effective thermal fault region in the image. The experimental results show that the proposed method exhibits good performance in region extraction and may be suitable for increasing the efficiency of automatic fault detection along transmission lines.
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Key words:
- MST framework /
- PCNN /
- transmission line /
- infrared image /
- clustering
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图 3 红外检测图像（第一行为原始图像，第二行为梯度图像）
Figure 3. Infrared images (First row: original images, Second row: gradient images)
图 9 本文PCNN方法第一次迭代结果
Figure 9. Segmentation results of our PCNN model at first PCNN iteration
表 1 阈值相似度评价
Table 1. Evaluation of threshold similarity
Image 1 Image 2 Image 3 Image 4 Image 5 Original value 0.4081 0.0910 0.5794 0.3050 0.3497 Final value 0.4440 0.1830 0.6010 0.3497 0.3358 
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表 2 时间复杂度度量
Table 2. Evaluation of time complex
s Image 1 Image 2 Image 3 Image 4 Image 5 Otsu 0.1395 0.1109 0.0028 0.0021 0.1245 MST 0.9271 2.8334 0.9680 0.2569 3.0357 PCNN 1.7231 14.9229 4.4648 1.2141 46.8639 Proposed 0.9528 6.0775 0.8206 0.2679 13.874
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