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A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Sensing and Imaging".

Deadline for manuscript submissions: closed (31 October 2023) | Viewed by 16384

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Prof. Dr. Ke Zhang

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Department of Electronic & Communication Engineering, School of Electrical and Electronic Engineering, North China Electric Power University, 619 Yonghuabei Dajie, Baoding 071000, China
Interests: computer vision; deep learning; power vision technology; facial attributes analysis
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Yincheng Qi

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Guest Editor

Department of Electronic & Communication Engineering, School of Electrical and Electronic Engineering, North China Electric Power University, 619 Yonghuabei Dajie, Baoding 071000, China
Interests: power vision technology; power information technology; deep learning
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Deep power vision technology is the application of deep learning-based computer vision technology in power systems and is an important component of power artificial intelligence technology. The electric power system is the important and key national infrastructure, and its safe and stable operation is related to the national economy and people’s livelihood as well as the sustainable development of the economy and society. At present, there is an increasing number of inspection images and videos obtained through vision sensors on helicopters, unmanned aerial vehicles, and robots. In order to improve the efficiency of power inspection and ensure the safe and stable operation of the electric power system, it has become a necessary and urgent task to apply computer vision and deep learning to visual processing of the goals and defects of power plants, transmission lines, substations, and distribution lines in electric power systems.

The goal of this Special Issue is to provide a platform for exchanges on research works, technical trends, and practical experience related to deep power vision technology and intelligent vision sensors. We are soliciting original papers of unpublished and completed research that is not currently under review by any other conference/magazine/journal. Topics of interest include but are not limited to the list below:

Deep learning-based computer vision technology in transmission or distribution line inspection
Deep learning-based computer vision technology in substation inspection
Deep learning-based computer vision technology in power plants inspection
Lightweight models for intelligent vision sensors
Model compression for intelligent vision sensors

Prof. Dr. Ke Zhang
Prof. Dr. Yincheng Qi
Guest Editors

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Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sensors is an international peer-reviewed open access semimonthly journal published by MDPI.

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Related Special Issue

Deep Power Vision Technology and Intelligent Vision Sensors: 2nd Edition in Sensors

Published Papers (11 papers)

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Editorial

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2 pages, 162 KiB

Open AccessEditorial

Deep Power Vision Technology and Intelligent Vision Sensors

by Ke Zhang and Yincheng Qi

Sensors 2023, 23(24), 9626; https://doi.org/10.3390/s23249626 - 05 Dec 2023

Viewed by 508

Abstract

With the rapid development of the power system and the increasing burden of its inspection, more attention has been paid to the automatic inspection technologies based on deep power vision technology and intelligent vision sensors [...] Full article

(This article belongs to the Special Issue Deep Power Vision Technology and Intelligent Vision Sensors)

Research

Jump to: Editorial

18 pages, 15967 KiB

Open AccessArticle

Multi-Patch Hierarchical Transmission Channel Image Dehazing Network Based on Dual Attention Level Feature Fusion

by Wenjiao Zai and Lisha Yan

Sensors 2023, 23(16), 7026; https://doi.org/10.3390/s23167026 - 08 Aug 2023

Cited by 2 | Viewed by 779

Abstract

Unmanned Aerial Vehicle (UAV) inspection of transmission channels in mountainous areas is susceptible to non-homogeneous fog, such as up-slope fog and advection fog, which causes crucial portions of transmission lines or towers to become fuzzy or even wholly concealed. This paper presents a Dual Attention Level Feature Fusion Multi-Patch Hierarchical Network (DAMPHN) for single image defogging to address the bad quality of cross-level feature fusion in Fast Deep Multi-Patch Hierarchical Networks (FDMPHN). Compared with FDMPHN before improvement, the Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index Measure (SSIM) of DAMPHN are increased by 0.3 dB and 0.011 on average, and the Average Processing Time (APT) of a single picture is shortened by 11%. Additionally, compared with the other three excellent defogging methods, the PSNR and SSIM values DAMPHN are increased by 1.75 dB and 0.022 on average. Then, to mimic non-homogeneous fog, we combine the single picture depth information with 3D Berlin noise to create the UAV-HAZE dataset, which is used in the field of UAV power assessment. The experiment demonstrates that DAMPHN offers excellent defogging results and is competitive in no-reference and full-reference assessment indices. Full article

(This article belongs to the Special Issue Deep Power Vision Technology and Intelligent Vision Sensors)

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15 pages, 4145 KiB

Open AccessArticle

Fittings Detection Method Based on Multi-Scale Geometric Transformation and Attention-Masking Mechanism

by Ning Wang, Ke Zhang, Jinwei Zhu, Liuqi Zhao, Zhenlin Huang, Xing Wen, Yuheng Zhang and Wenshuo Lou

Sensors 2023, 23(10), 4923; https://doi.org/10.3390/s23104923 - 19 May 2023

Cited by 2 | Viewed by 829

Abstract

Overhead transmission lines are important lifelines in power systems, and the research and application of their intelligent patrol technology is one of the key technologies for building smart grids. The main reason for the low detection performance of fittings is the wide range of some fittings’ scale and large geometric changes. In this paper, we propose a fittings detection method based on multi-scale geometric transformation and attention-masking mechanism. Firstly, we design a multi-view geometric transformation enhancement strategy, which models geometric transformation as a combination of multiple homomorphic images to obtain image features from multiple views. Then, we introduce an efficient multiscale feature fusion method to improve the detection performance of the model for targets with different scales. Finally, we introduce an attention-masking mechanism to reduce the computational burden of model-learning multiscale features, thereby further improving model performance. In this paper, experiments have been conducted on different datasets, and the experimental results show that the proposed method greatly improves the detection accuracy of transmission line fittings. Full article

(This article belongs to the Special Issue Deep Power Vision Technology and Intelligent Vision Sensors)

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14 pages, 2805 KiB

Open AccessArticle

Zero-Shot Image Classification Method Based on Attention Mechanism and Semantic Information Fusion

by Yaru Wang, Lilong Feng, Xiaoke Song, Dawei Xu and Yongjie Zhai

Sensors 2023, 23(4), 2311; https://doi.org/10.3390/s23042311 - 19 Feb 2023

Cited by 1 | Viewed by 1825

Abstract

The zero-shot image classification (ZSIC) is designed to solve the classification problem when the sample is very small, or the category is missing. A common method is to use attribute or word vectors as a priori category features (auxiliary information) and complete the domain transfer from training of seen classes to recognition of unseen classes by building a mapping between image features and a priori category features. However, feature extraction of the whole image lacks discrimination, and the amount of information of single attribute features or word vector features of categories is insufficient, which makes the matching degree between image features and prior class features not high and affects the accuracy of the ZSIC model. To this end, a spatial attention mechanism is designed, and an image feature extraction module based on this attention mechanism is constructed to screen critical features with discrimination. A semantic information fusion method based on matrix decomposition is proposed, which first decomposes the attribute features and then fuses them with the extracted word vector features of a dataset to achieve information expansion. Through the above two improvement measures, the classification accuracy of the ZSIC model for unseen images is improved. The experimental results on public datasets verify the effect and superiority of the proposed methods. Full article

(This article belongs to the Special Issue Deep Power Vision Technology and Intelligent Vision Sensors)

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11 pages, 4017 KiB

Open AccessArticle

A New Orientation Detection Method for Tilting Insulators Incorporating Angle Regression and Priori Constraints

by Jianli Zhao, Liangshuai Liu, Ze Chen, Yanpeng Ji and Haiyan Feng

Sensors 2022, 22(24), 9773; https://doi.org/10.3390/s22249773 - 13 Dec 2022

Cited by 3 | Viewed by 1024

Abstract

The accurate detection of insulators is an important prerequisite for insulator fault diagnosis. To solve the problem of background interference and overlap caused by the axis-aligned bounding boxes in the tilting insulator detection tasks, we construct an improved detection architecture according to the scale and tilt features of the insulators from several perspectives, such as bounding box representation, loss function, and anchor box construction. A new orientation detection method for tilting insulators based on angle regression and priori constraints is put forward in this paper. Ablation tests and comparative validation tests were conducted on a self-built aerial insulator image dataset. The results show that the detection accuracy of our model was increased by 7.98% compared with that of the baseline, and the overall detection accuracy reached 82.33%. Moreover, the detection effect of our method was better than that of the YOLOv5 detection model and other orientation detection models. Our model provides a new idea for the accurate orientation detection of insulators. Full article

(This article belongs to the Special Issue Deep Power Vision Technology and Intelligent Vision Sensors)

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12 pages, 2329 KiB

Open AccessArticle

A New Bolt Defect Identification Method Incorporating Attention Mechanism and Wide Residual Networks

by Liangshuai Liu, Jianli Zhao, Ze Chen, Baijie Zhao and Yanpeng Ji

Sensors 2022, 22(19), 7416; https://doi.org/10.3390/s22197416 - 29 Sep 2022

Cited by 3 | Viewed by 1480

Abstract

Bolts are important components on transmission lines, and the timely detection and exclusion of their abnormal conditions are imperative to ensure the stable operation of transmission lines. To accurately identify bolt defects, we propose a bolt defect identification method incorporating an attention mechanism and wide residual networks. Firstly, the spatial dimension of the feature map is compressed by the spatial compression network to obtain the global features of the channel dimension and enhance the attention of the network to the vital information in a weighted way. After that, the enhanced feature map is decomposed into two one-dimensional feature vectors by embedding a cooperative attention mechanism to establish long-term dependencies in one spatial direction and preserve precise location information in the other direction. During this process, the prior knowledge of the bolts is utilized to help the network extract critical feature information more accurately, thus improving the accuracy of recognition. The test results show that the bolt recognition accuracy of this method is improved to 94.57% compared with that before embedding the attention mechanism, which verifies the validity of the proposed method. Full article

(This article belongs to the Special Issue Deep Power Vision Technology and Intelligent Vision Sensors)

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16 pages, 11672 KiB

Open AccessArticle

Two-Level Model for Detecting Substation Defects from Infrared Images

by Bing Li, Tian Wang, Zhedong Hu, Chao Yuan and Yongjie Zhai

Sensors 2022, 22(18), 6861; https://doi.org/10.3390/s22186861 - 10 Sep 2022

Cited by 5 | Viewed by 1839

Abstract

Training a deep convolutional neural network (DCNN) to detect defects in substation equipment often requires many defect datasets. However, this dataset is not easily acquired, and the complex background of the infrared images makes defect detection even more difficult. To alleviate this issue, this article presents a two-level defect detection model (TDDM). First, to extract the target equipment in the image, an instance segmentation module is constructed by training from the instance segmentation dataset. Then, the target equipment is segmented by the superpixel segmentation algorithm into superpixels according to obtain more details information. Next, a temperature probability density distribution is constructed with the superpixels, and the defect determination strategy is used to recognize the defect. Finally, experiments verify the effectiveness of the TDDM according to the defect detection dataset. Full article

(This article belongs to the Special Issue Deep Power Vision Technology and Intelligent Vision Sensors)

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14 pages, 5972 KiB

Open AccessArticle

Transmission Line Object Detection Method Based on Contextual Information Enhancement and Joint Heterogeneous Representation

by Lijuan Zhao, Chang’an Liu and Hongquan Qu

Sensors 2022, 22(18), 6855; https://doi.org/10.3390/s22186855 - 10 Sep 2022

Cited by 3 | Viewed by 1132

Abstract

Transmission line inspection plays an important role in maintaining power security. In the object detection of the transmission line, the large-scale gap of the fittings is still a main and negative factor in affecting the detection accuracy. In this study, an optimized method is proposed based on the contextual information enhancement (CIE) and joint heterogeneous representation (JHR). In the high-resolution feature extraction layer of the Swin transformer, the convolution is added in the part of the self-attention calculation, which can enhance the contextual information features and improve the feature extraction ability for small objects. Moreover, in the detection head, the joint heterogeneous representations of different detection methods are combined to enhance the features of classification and localization tasks, which can improve the detection accuracy of small objects. The experimental results show that this optimized method has a good detection performance on the small-sized and obscured objects in the transmission line. The total mAP (mean average precision) of the detected objects by this optimized method is increased by 5.8%, and in particular, the AP of the normal pin is increased by 18.6%. The improvement of the accuracy of the transmission line object detection method lays a foundation for further real-time inspection. Full article

(This article belongs to the Special Issue Deep Power Vision Technology and Intelligent Vision Sensors)

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14 pages, 5664 KiB

Open AccessArticle

Improved Algorithm for Insulator and Its Defect Detection Based on YOLOX

by Gujing Han, Tao Li, Qiang Li, Feng Zhao, Min Zhang, Ruijie Wang, Qiwei Yuan, Kaipei Liu and Liang Qin

Sensors 2022, 22(16), 6186; https://doi.org/10.3390/s22166186 - 18 Aug 2022

Cited by 19 | Viewed by 2600

Abstract

Aerial insulator defect images have some features. For instance, the complex background and small target of defects would make it difficult to detect insulator defects quickly and accurately. To solve the problem of low accuracy of insulator defect detection, this paper concerns the shortcomings of IoU and the sensitivity of small targets to the model regression accuracy. An improved SIoU loss function was proposed based on the regular influence of regression direction on the accuracy. This loss function can accelerate the convergence of the model and make it achieve better results in regressions. For complex backgrounds, ECA (Efficient Channel Attention Module) is embedded between the backbone and the feature fusion layer of the model to reduce the influence of redundant features on the detection accuracy and make progress in the aspect. As a result, these experiments show that the improved model achieved 97.18% mAP which is 2.74% higher than before, and the detection speed could reach 71 fps. To some extent, it can detect insulator and its defects accurately and in real-time. Full article

(This article belongs to the Special Issue Deep Power Vision Technology and Intelligent Vision Sensors)

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15 pages, 1494 KiB

Open AccessArticle

Multi-Geometric Reasoning Network for Insulator Defect Detection of Electric Transmission Lines

by Yongjie Zhai, Zhedong Hu, Qianming Wang, Qiang Yang and Ke Yang

Sensors 2022, 22(16), 6102; https://doi.org/10.3390/s22166102 - 15 Aug 2022

Cited by 1 | Viewed by 1188

Abstract

To address the challenges in the unmanned system-based intelligent inspection of electric transmission line insulators, this paper proposed a multi-geometric reasoning network (MGRN) to accurately detect insulator geometric defects based on aerial images with complex backgrounds and different scales. The spatial geometric reasoning sub-module (SGR) was developed to represent the spatial location relationship of defects. The appearance geometric reasoning sub-module (AGR) and the parallel feature transformation (PFT) sub-module were adopted to obtain the appearance geometric features from the real samples. These multi-geometric features can be fused with the original visual features to identify and locate the insulator defects. The proposed solution is assessed through experiments against the existing solutions and the numerical results indicate that it can significantly improve the detection accuracy of multiple insulator defects using the aerial images. Full article

(This article belongs to the Special Issue Deep Power Vision Technology and Intelligent Vision Sensors)

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17 pages, 5531 KiB

Open AccessArticle

Insulator Umbrella Disc Shedding Detection in Foggy Weather

by Rui Xin, Xi Chen, Junying Wu, Ke Yang, Xinying Wang and Yongjie Zhai

Sensors 2022, 22(13), 4871; https://doi.org/10.3390/s22134871 - 28 Jun 2022

Cited by 6 | Viewed by 1439

Abstract

The detection of insulator umbrella disc shedding is very important to the stable operation of a transmission line. In order to accomplish the accurate detection of the insulator umbrella disc shedding in foggy weather, a two-stage detection model combined with a defogging algorithm is proposed. In the dehazing stage of insulator images, solving the problem of real hazy image data is difficult; the foggy images are dehazed by the method of synthetic foggy images training and real foggy images fine-tuning. In the detection stage of umbrella disc shedding, a small object detection algorithm named FA-SSD is proposed to solve the problem of the umbrella disc shedding occupying only a small proportion of an aerial image. On the one hand, the shallow feature information and deep feature information are fused to improve the feature extraction ability of small targets; on the other hand, the attention mechanism is introduced to strengthen the feature extraction network’s attention to the details of small targets and improve the model’s ability to detect the umbrella disc shedding. The experimental results show that our model can accurately detect the insulator umbrella disc shedding defect in the foggy image; the accuracy of the defect detection is 0.925, and the recall is 0.841. Compared with the original model, it improved by 5.9% and 8.6%, respectively. Full article

(This article belongs to the Special Issue Deep Power Vision Technology and Intelligent Vision Sensors)

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