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Finding region of interest in the infrared image of electrical installation

机译:在电气安装的红外图像中找到感兴趣的区域

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This paper presents a method of automatically finding the region of interests (ROIs) in an infrared image of electrical installations. These regions are very important, particularly for diagnosing the thermal condition of electrical equipment. For a vast number of electrical equipment to be inspected, manual region selection of the images normally will take a lot of time. Therefore, an automatic region detection system is more preferred. However, due to the nature of the infrared image, the conventional segmentation methods have some limitations in order to properly find the desired ROIs. In addition, all objects within the image commonly have heterogeneous pixel intensities causing the segmented regions tend to be over segmented or some parts of the target objects either be divided into multiple regions or merging with the background image. Therefore, this paper proposes a new segmentation method of detecting the repeated structures of electrical equipment within an infrared image by taking the advantage of local keypoint feature matching. Experimental results indicate that the proposed method achieves a better performance for detecting the target ROIs compared with the conventional methods. The algorithm was tested on real infrared images with diverse irregular intensity variations and cluttered background. Finally, the performance of the proposed method was qualitatively and quantitatively evaluated. (C) 2015 Elsevier B.V. All rights reserved.
机译:本文提出了一种在电气设备的红外图像中自动查找感兴趣区域(ROI)的方法。这些区域非常重要,特别是对于诊断电气设备的热状况。对于要检查的大量电气设备,手动选择图像区域通常会花费很多时间。因此,更优选自动区域检测系统。然而,由于红外图像的性质,常规的分割方法具有一些限制,以便正确地找到期望的ROI。另外,图像内的所有对象通常具有异质的像素强度,导致分割的区域趋于过度分割,或者目标对象的某些部分被分为多个区域或与背景图像合并。因此,本文提出了一种利用局部关键点特征匹配的方法检测红外图像中电气设备重复结构的新分割方法。实验结果表明,与常规方法相比,该方法具有更好的目标ROI检测性能。该算法已在具有各种不规则强度变化和杂乱背景的真实红外图像上进行了测试。最后,对所提方法的性能进行了定性和定量评估。 (C)2015 Elsevier B.V.保留所有权利。

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