Intelligent Diagnosis of Incipient Fault in Power Distribution Lines Based on Corona Detection in UV-Visible Videos

Davari, Noushin; Akbarizadeh, Gholamreza; Mashhour, Elaheh

doi:10.1109/tpwrd.2020.3046161

Cited by 79 publications

(33 citation statements)

References 44 publications

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“…Road information extraction via RS image. Traditionally, processing of RS image data is using efficient exposure features, texture characteristics and so on to obtain the target information through specific methods, such as wavelet transform [23,24], In recent years, inspired by the performance of state-of-the-art deep learning technology in the computer vision community, such as image classification, object detection [25,26] and image segmentation [27], deep learning has been also incorporated into RS image processing and obtained appealing results. Road information extraction is a research hotpot in RS field.…”

Section: Related Workmentioning

confidence: 99%

A Guided Deep Learning Approach for Joint Road Extraction and Intersection Detection From RS Images and Taxi Trajectories

Xiang

Cai-li

et al. 2021

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Automatic extraction of road information based on data-driven methods is significant for various practical applications. Remote sensing (RS) images and GPS trajectories are two available data sources that can describe roads from a complementary perspective, and fusing them together can improve road detection performance. However, existing studies on the combination of RS images and GPS trajectories do not fully utilize their enhanced information about roads and suffer from road information loss. Moreover, roads and intersections are two crucial elements of road network generation, and they are closely related to each other. Therefore, we propose a multi-task and multi-source adaptive fusion (MTMSAF) network, which leverages RS images and trajectory datasets to execute road extraction tasks and intersection detection tasks simultaneously. The MTMSAF network is built on three components. First, two encoder steams are designed to capture road features from RS images and trajectories. Then, an adaptive fusion module is created to fuse the individual road features at each level in a guiding fashion. Finally, two specific decoders are proposed: one is used to implement the road region detection task by considering multidirectional geometric road information, and the other is used to perform the intersection extraction task by recovering intersection information with the road region feature. Four stateof-the-art deep learning-based methods, including segmentation networks and road extraction networks, are compared with the proposed approach. The results show the superiority of our approach for both road detection and intersection extraction tasks.

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Section: Related Workmentioning

confidence: 99%

A Guided Deep Learning Approach for Joint Road Extraction and Intersection Detection From RS Images and Taxi Trajectories

Xiang

Cai-li

et al. 2021

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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“…In recent years, deep learning has been gradually applied to the field of geoscience and remote sensing [4], [5]. The deep learning methods can automatically extract features of different levels and learn from context.…”

Section: Introductionmentioning

confidence: 99%

A Correlation Context-Driven Method for Sea Fog Detection in Meteorological Satellite Imagery

Huang

Guo

et al. 2022

IEEE Geosci. Remote Sensing Lett.

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Sea fog detection is a challenging and essential issue in satellite remote sensing. Although conventional threshold methods and deep learning methods can achieve pixel-level classification, it is difficult to distinguish ambiguous boundaries and thin structures from the background. Considering the correlations between neighbor pixels and the affinities between superpixels, a correlation context-driven method for sea fog detection is proposed in this letter, which mainly consists of a two-stage superpixel-based fully convolutional network (SFCNet), named SFCNet. A fully connected Conditional Random Field (CRF) is utilized to model the dependencies between pixels. To alleviate the problem of high cloud occlusion, an attentive Generative Adversarial Network (GAN) is implemented for image enhancement by exploiting contextual information. Experimental results demonstrate that our proposed method achieves 91.65% mIoU and obtains more refined segmentation results, performing well in detecting fogs in small, broken bits and weak contrast thin structures, as well as detects more obscured parts.

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“…The method is capable of achieving accuracy and reproducibility comparable to that accomplished by humans but requires a considerably less amount of time to generate the TEM data [27]. By combining traditional image processing and the latest convolutional neural network techniques, objects can be detected with improved exactness [28,29]. To further enhance the detection precision, large datasets with qualified annotations are normally required for training and testing deep neural networks.…”

Section: Introductionmentioning

confidence: 99%

Statistical analysis of helium bubbles in transmission electron microscopy images based on machine learning method

Bai

Zhang

et al. 2021

NUCL SCI TECH

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Helium bubbles, which are typical radiation microstructures observed in metals or alloys, are usually investigated using transmission electron microscopy (TEM). However, the investigation requires human inputs to locate and mark the bubbles in the acquired TEM images, rendering this task laborious and prone to error. In this paper, a machine learning method capable of automatically identifying and analyzing TEM images of helium bubbles is proposed, thereby improving the efficiency and reliability of the investigation. In the proposed technique, helium bubble clusters are first determined via the density-based spatial clustering of applications with noise algorithm after removing the background and noise pixels. For each helium bubble cluster, the number of helium bubbles is determined based on the cluster size depending on the specific image resolution. Finally, the helium bubble clusters are analyzed using a Gaussian mixture model, yielding the location and size information on the helium bubbles. In contrast to other approaches that require training using numerous annotated images to establish an accurate classifier, the parameters used in the established model are determined using a small number of TEM images. The results of the model formulated according to the proposed approach achieved a higher F1 score validated through some helium bubble images manually marked. Furthermore, the established model can identify bubble-like objects that humans cannot facilely identify. This computationally efficient method achieves object recognition for material structure identification that may be advantageous to scientific work.

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Intelligent Diagnosis of Incipient Fault in Power Distribution Lines Based on Corona Detection in UV-Visible Videos

Cited by 79 publications

References 44 publications

A Guided Deep Learning Approach for Joint Road Extraction and Intersection Detection From RS Images and Taxi Trajectories

A Guided Deep Learning Approach for Joint Road Extraction and Intersection Detection From RS Images and Taxi Trajectories

A Correlation Context-Driven Method for Sea Fog Detection in Meteorological Satellite Imagery

Statistical analysis of helium bubbles in transmission electron microscopy images based on machine learning method

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