scholarly journals Corrigendum: Insulator Contamination Perception Based on Feature Fusion of Infrared Image and Meteorological Parameters

2021 ◽  
Vol 9 ◽  
Author(s):  
Hongxia Wang ◽  
Bo Wang ◽  
Min Li ◽  
Peng Luo ◽  
Hengrui Ma ◽  
...  
Keyword(s):  
Meteorological Parameters ◽  
Feature Fusion ◽  
Infrared Image

scholarly journals Insulator Contamination Perception Based on Feature Fusion of Infrared Image and Meteorological Parameters

2021 ◽  
Vol 9 ◽  
Author(s):  
Hongxia Wang ◽  
Bo Wang ◽  
Min Li ◽  
Peng Luo ◽  
Hengrui Ma ◽  
...  
Keyword(s):  
Feature Extraction ◽  
Meteorological Parameters ◽  
Feature Fusion ◽  
Low Cost ◽  
Infrared Image ◽  
Weather Conditions ◽  
Power Grids ◽  
Pollution Level ◽  
Stable Operation ◽  
Fusion Model

Polluted insulators seriously threaten the safe and stable operation of power grids, which attaches great significance to insulator contamination perception. Among the present methods, the non-contact approaches based on infrared images have gradually been widely used, as they are much more safe and are of low cost. However, the thermal effect of insulators is largely affected by meteorological conditions, which makes the infrared image-based methods less accurate. To solve the above problem, we take infrared image and meteorological parameters including humidity and temperature as input, and propose a feature fusion model to perceive insulator contamination in different weather conditions. Firstly, different feature extraction networks are used to perform feature extraction on the two types of data; secondly, the two features are concatenated to fuse together; thirdly, further feature extraction is performed and contamination is classified according to the pollution severity. Case studies show that the proposed method can better explore the relationship between humidity, temperature and pollution level of the insulators, thus can better separate the contamination grades and outperform the conventional infrared image based methods.

scholarly journals MS-AFF: A Novel Semantic Segmentation Approach for Buried Object Based on Multi-scale Attentional Feature Fusion

2021 ◽  
Author(s):  
Chao Lu ◽  
Fansheng Chen ◽  
Xiaofeng Su ◽  
Dan Zeng
Keyword(s):  
Deep Learning ◽  
Spatial Information ◽  
Feature Fusion ◽  
Infrared Image ◽  
Semantic Segmentation ◽  
Target Object ◽  
Infrared Images ◽  
Feature Maps ◽  
Multi Scale ◽  
Visible Images

Abstract Infrared technology is a widely used in precision guidance and mine detection since it can capture the heat radiated outward from the target object. We use infrared (IR) thermography to get the infrared image of the buried obje cts. Compared to the visible images, infrared images present poor resolution, low contrast, and fuzzy visual effect, which make it difficult to segment the target object, specifically in the complex backgrounds. In this condition, traditional segmentation methods cannot perform well in infrared images since they are easily disturbed by the noise and non-target objects in the images. With the advance of deep convolutional neural network (CNN), the deep learning-based methods have made significant improvements in semantic segmentation task. However, few of them research Infrared image semantic segmentation, which is a more challenging scenario compared to visible images. Moreover, the lack of an Infrared image dataset is also a problem for current methods based on deep learning. We raise a multi-scale attentional feature fusion (MS-AFF) module for infrared image semantic segmentation to solve this problem. Precisely, we integrate a series of feature maps from different levels by an atrous spatial pyramid structure. In this way, the model can obtain rich representation ability on the infrared images. Besides, a global spatial information attention module is employed to let the model focus on the target region and reduce disturbance in infrared images' background. In addition, we propose an infrared segmentation dataset based on the infrared thermal imaging system. Extensive experiments conducted in the infrared image segmentation dataset show the superiority of our method.

scholarly journals MSIS: Multispectral Instance Segmentation Method for Power Equipment

2022 ◽  
Vol 2022 ◽  
pp. 1-13
Author(s):  
Jun Shu ◽  
Juncheng He ◽  
Ling Li
Keyword(s):  
Feature Extraction ◽  
Fault Detection ◽  
Feature Fusion ◽  
Infrared Image ◽  
Current Method ◽  
Power Equipment ◽  
Infrared Images ◽  
Low Contrast ◽  
Novel Structure ◽  
Instance Segmentation

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.

A Study on Utilization of Three-Dimensional Sensor Lip Image for Developing a Pronunciation Recognition System

2019 ◽  
Vol 63 (5) ◽  
pp. 50402-1-50402-9 ◽  
Author(s):  
Ing-Jr Ding ◽  
Chong-Min Ruan
Keyword(s):  
Principal Component Analysis ◽  
Automatic Speech Recognition ◽  
Feature Fusion ◽  
Three Dimensional ◽  
Principal Component ◽  
Recognition System ◽  
Geometrical Characteristics ◽  
3D Geometry ◽  
Different Types ◽  
The Disabled

Abstract The acoustic-based automatic speech recognition (ASR) technique has been a matured technique and widely seen to be used in numerous applications. However, acoustic-based ASR will not maintain a standard performance for the disabled group with an abnormal face, that is atypical eye or mouth geometrical characteristics. For governing this problem, this article develops a three-dimensional (3D) sensor lip image based pronunciation recognition system where the 3D sensor is efficiently used to acquire the action variations of the lip shapes of the pronunciation action from a speaker. In this work, two different types of 3D lip features for pronunciation recognition are presented, 3D-(x, y, z) coordinate lip feature and 3D geometry lip feature parameters. For the 3D-(x, y, z) coordinate lip feature design, 18 location points, each of which has 3D-sized coordinates, around the outer and inner lips are properly defined. In the design of 3D geometry lip features, eight types of features considering the geometrical space characteristics of the inner lip are developed. In addition, feature fusion to combine both 3D-(x, y, z) coordinate and 3D geometry lip features is further considered. The presented 3D sensor lip image based feature evaluated the performance and effectiveness using the principal component analysis based classification calculation approach. Experimental results on pronunciation recognition of two different datasets, Mandarin syllables and Mandarin phrases, demonstrate the competitive performance of the presented 3D sensor lip image based pronunciation recognition system.

Sign in / Sign up

Export Citation Format

Share Document