Corona Detection and Power Equipment Classification Based on GoogleNet-AlexNet: An Accurate and Intelligent Defect Detection Model Based on Deep Learning for Power Distribution Lines

Davari, Noushin; Akbarizadeh, Gholamreza; Mashhour, Elaheh

doi:10.1109/tpwrd.2021.3116489

Cited by 59 publications

(16 citation statements)

References 25 publications

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“…Classic methods extract features using expert experience to search for ships [1], [2], e.g., constant false alarm rate (CFAR) [3] and template-based matching [4]. Modern methods use convolutional neural networks (CNNs) [5], [6] to extract features with less human involvement, receiving more attention in SAR community. Most scholars still used rectangular bounding boxes [7]- [11] to detect ships; SAR ship instance segmentation at the box-and pixel-level received less attention.…”

Section: Introductionmentioning

confidence: 99%

A Mask Attention Interaction and Scale Enhancement Network for SAR Ship Instance Segmentation

Zhang

2022

IEEE Geosci. Remote Sensing Lett.

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Most of existing synthetic aperture radar (SAR) ship instance segmentation models do not achieve mask interaction or offer limited interaction performance. Besides, their multi-scale ship instance segmentation performance is moderate especially for small ships. To solve these problems, we propose a mask attention interaction and scale enhancement network (MAI-SE-Net) for SAR ship instance segmentation. MAI uses an atrous spatial pyramid pooling (ASPP) to gain multi-resolution feature responses, a non-local block (NLB) to model long-range spatial dependencies, and a concatenation shuffle attention block (CSAB) to improve interaction benefits. SE uses a content-aware reassembly of features block (CARAFEB) to generate an extra pyramid bottom-level to boost small ship performance, a feature balance operation (FBO) to improve scale feature description, and a global context block (GCB) to refine features. Experimental results on two public SSDD and HRSID datasets reveal that MAI-SE-Net outperforms the other nine competitive models, better than the suboptimal model by 4.7% detection AP and 3.4% segmentation AP on SSDD and by 3.0% detection AP and 2.4% segmentation AP on HRSID.

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Section: Introductionmentioning

confidence: 99%

A Mask Attention Interaction and Scale Enhancement Network for SAR Ship Instance Segmentation

Zhang

2022

IEEE Geosci. Remote Sensing Lett.

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“…[23] Davari et al have suggested a practical automatic diagnostic technique using CoroCam 6D2 camera for detection of Corona and power equipment classification using AlexNet and GoogLeNet networks. [24] Ibrahim et al implemented machine learning in the detection of the severity of erosion in insulators' surfaces as per IEC-60587 standards. [25] Though literature is available in the field of defect detection and property enhancement methods of room temperature vulcanized silicone rubber (RTV SR) cables yet, reports on the effect of exposition of nano/micro doped RTV silicone rubber cables to high dose gammaray irradiation are still scanty.…”

Section: Introductionmentioning

confidence: 99%

Performance evaluation of gamma‐ray irradiated silicone rubber nano‐micro composites using electrical, thermal, physiochemical and deep learning techniques

et al. 2022

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Silicone rubber insulation used in nuclear power stations is inevitably exposed to highly energetic gamma-ray irradiations which may lead to premature failure. Therefore, it is mandatory to develop silicone rubber composites capable of withstanding the stress posed by gamma-ray irradiation. The present research involves the development of micro aluminum tri hydrate (ATH) and nano silica (SiO 2 ) doped/co-doped silicone rubber nanocomposites best suited for use in nuclear power stations. The samples were exposed to gamma-ray irradiation of 500 kGy. Surface changes incurred were assessed using atomic force microscopy (AFM), fourier transform infrared (FTIR) spectroscopy, contact angle, surface potential/impedance, water droplet-initiated corona inception voltage (CIV) measurements thermogravimetric analysis (TGA) was adopted to characterize the thermal stability of the specimens. Further moisture absorption behavior of the test specimens under strong electrolytic NaCl solution has been investigated. The erosion resistance of the virgin and gamma-ray irradiated specimens has been classified using GoogLeNet convolution neural network.

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“…1 In recent years, researchers have made considerable progress in sea surface target detection. 2 Radar is one of the sensors for moving target detection on the sea surface. It can realize moving target detection and tracking through transmitting electromagnetic wave and receiving scene echoes.…”

Section: Introductionmentioning

confidence: 99%

“…It is well known that moving target detection and tracking have important military and civil values in real applications 1 . In recent years, researchers have made considerable progress in sea surface target detection 2 . Radar is one of the sensors for moving target detection on the sea surface.…”

Section: Introductionmentioning

confidence: 99%

Scheme to implement moving target detection of coastal defense radar in complicated sea conditions

Hou

Zhang

et al. 2022

J. Appl. Rem. Sens.

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.Although the data on a particular complicated sea conditions are available, it is difficult to collect the data on various complicated sea conditions simultaneously. When the sea conditions change, the test results of the detection network based on the data of simple sea conditions may be unacceptable. It is proposed that the VGG16, pix2pixHD, and cycleGAN methods should be applied to establish related datasets of different complicated sea conditions. After the unified image quality assessment indicators are determined, it is found that the images generated by the VGG16 network are most in line with the subsequent target detection standards. The real images of complicated sea conditions have the smallest root-mean-square error, the largest peak signal-to-noise ratio, and the best regression coefficient (R2). Meanwhile, the improved Faster R-CNN is introduced for target detection of small sample datasets. First, the ResNet50_FPN module, as the backbone feature extractor, is used to improve the detection performance of small-sized objects. Moreover, because there are many small target objects to be detected, the ROI Align module is preferred, for it is more accurate. Finally, the Softer-NMS algorithm is selected to significantly improve the positioning accuracy through confidence estimation. Compared with some previous Faster R-CNN methods, the accuracy and missed detection rate of the proposed method outperform other networks, with a mean average precision of 85.11%.

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Corona Detection and Power Equipment Classification Based on GoogleNet-AlexNet: An Accurate and Intelligent Defect Detection Model Based on Deep Learning for Power Distribution Lines

Cited by 59 publications

References 25 publications

A Mask Attention Interaction and Scale Enhancement Network for SAR Ship Instance Segmentation

A Mask Attention Interaction and Scale Enhancement Network for SAR Ship Instance Segmentation

Performance evaluation of gamma‐ray irradiated silicone rubber nano‐micro composites using electrical, thermal, physiochemical and deep learning techniques

Scheme to implement moving target detection of coastal defense radar in complicated sea conditions

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