Abstract
Infrared image of power equipment is widely used in power equipment fault detection, and segmentation of infrared images is an important step in power equipment thermal fault detection. Nevertheless, since the overlap of the equipment, the complex background, and the low contrast of the infrared image, the current method still cannot complete the detection and segmentation of the power equipment well. To better segment the power equipment in the infrared image, in this paper, a multispectral instance segmentation (MSIS) based on SOLOv2 is designed, which is an end-to-end and single-stage network. First, we provide a novel structure of multispectral feature extraction, which can simultaneously obtain rich features in visible images and infrared images. Secondly, a module of feature fusion (MARFN) has been constructed to fully obtain fusion features. Finally, the combination of multispectral feature extraction, the module of feature fusion (MARFN), and instance segmentation (SOLOv2) realize multispectral instance segmentation of power equipment. The experimental results show that the proposed MSIS model has an excellent performance in the instance segmentation of power equipment. The MSIS based on ResNet-50 has 40.06% AP.
Highlights
In the fault detection of power systems, infrared imaging technology has the characteristics of operationally simple, fast response speed, and accurate judgment; it has become an important tool for the systems of failure detection [1]
To solve the above problems, this research has collected and set up power equipment image datasets, it is aimed that the complete segmentation of power equipment was realized, and a multispectral instance segmentation is designed to directly complete the classification, positioning, and pixel segmentation of power equipment. e main contributions of this work are as follows: (1) We propose a multispectral single-stage instance segmentation (MSIS) network based on SOLOv2. e method integrates image fusion and instance segmentation into a single network. e network may ensure the real-time performance of segmentation while reducing structural redundancy caused by multitasking
In the experiment of the multispectral instance segmentation, we used the method [47] to obtain the final registration image. e multispectral image consists of 2940 pairs of arresters and 2998 pairs of transformers. e division ratio of the training set, validation set, and test set is 6 : 2 : 2, and the distribution results of the power equipment dataset during training are shown in Table 4. e dataset is manually labeled by LabelMe
Summary
In the fault detection of power systems, infrared imaging technology has the characteristics of operationally simple, fast response speed, and accurate judgment; it has become an important tool for the systems of failure detection [1]. In the traditional segmentation method, Zhou et al extract potential regions of faults by superpixel segmentation method, and the residual network has used to screen the real position of fault [2]. E fuzzy C-means (FCM) clustering algorithm was used to suppress the oversegmentation, and the overheated area was accurately divided [3]. In the machine learning method, Xu et al proposed a fault region extraction method based on a pulse-coupled neural network (PCNN). Shanmugam and Chandira Sekaran used the FCM clustering algorithm to segment infrared images, and the Modified Ant Lion Optimization (MALO) and Region Pros function are used to optimize the segmentation area [4]. Qi et al proposed a new method of infrared image segmentation based on a multiinformation fused fuzzy clustering method. Qi et al proposed a new method of infrared image segmentation based on a multiinformation fused fuzzy clustering method. is method
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