A Segmentation-Based Multitask Learning Approach for Isolating Switch State Recognition in High-Speed Railway Traction Substation

Lu, Xuemin; Quan, Wei; Gao, Shibin; Zhang, Guangxiao; Feng, Kuan; Lin, Guosong; Chen, Jim X.

doi:10.1109/tits.2022.3146338

Cited by 11 publications

(3 citation statements)

References 41 publications

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“…Such systems can play a vital role in ensuring the smooth functioning of electrical grids and preventing potential equipment failures that can result in power outages and safety hazards [4]. As the existing literature lacks comprehensive and well-annotated datasets specifically tailored to the unique challenges and characteristics of electrical substations, works such as [5] (that focused on semantic segmentation of electrical switches), [6] (that focused on semantic segmentation of current transformers on thermograms) and [7] (that focused on semantic segmentation of insulators) had to make their own datasets to train their segmentation models.…”

Section: Discussionmentioning

confidence: 99%

A Semantically Annotated 15-Class Ground Truth Dataset for Substation Equipment to Train Semantic Segmentation Models

Gomes

Ganacim

Magrin

et al. 2023

Data

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The lack of annotated semantic segmentation datasets for electrical substations in the literature poses a significant problem for machine learning tasks; before training a model, a dataset is needed. This paper presents a new dataset of electric substations with 1660 images annotated with 15 classes, including insulators, disconnect switches, transformers and other equipment commonly found in substation environments. The images were captured using a combination of human, fixed and AGV-mounted cameras at different times of the day, providing a diverse set of training and testing data for algorithm development. In total, 50,705 annotations were created by a team of experienced annotators, using a standardized process to ensure accuracy across the dataset. The resulting dataset provides a valuable resource for researchers and practitioners working in the fields of substation automation, substation monitoring and computer vision. Its availability has the potential to advance the state of the art in this important area.

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Section: Discussionmentioning

confidence: 99%

A Semantically Annotated 15-Class Ground Truth Dataset for Substation Equipment to Train Semantic Segmentation Models

Gomes

Ganacim

Magrin

et al. 2023

Data

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“…Today, machine-learning-based segmentation techniques have been widely applied not only to cell tracking, 9 brain tumour segmentation, 10 autonomous driving, 11,12 geographic, [13][14][15] and also material science. 16 DeCost et al 17 adopted the ''bag of visual feature'' image representation for Support Vector Machine SVM model to perform microstructure classification.…”

Section: Introductionmentioning

confidence: 99%

Accurate identification and measurement of the precipitate area by two-stage deep neural networks in novel chromium-based alloys

Xia

Cheng

et al. 2023

Phys. Chem. Chem. Phys.

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“…Huang et al [ 14 ] identified the working state of isolation switches based on YOLO version 4 (YOLOv4) [ 15 ] through robot inspection images, even in cases of foggy or rainy weather. Similarly, Lu et al [ 16 ] proposed a segmentation-based network to recognize the working state of the isolating switch in the traction substation; their experiment results suggest the pixel-level segmentation approach exhibits more fine-grained feature extraction capability. Huang et al [ 17 ] also utilized image segmentation methods to separate the damper from its complex background to accurately recognize the damper rust status.…”

Section: Introductionmentioning

confidence: 99%

An Identification Method for Irregular Components Related to Terminal Blocks in Equipment Cabinet of Power Substation

Cao,

Chen,

Deng

et al. 2023

Sensors

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Despite the continuous advancement of intelligent power substations, the terminal block components within equipment cabinet inspection work still often require loads of personnel. The repetitive documentary works not only lack efficiency but are also susceptible to inaccuracies introduced by substation personnel. To resolve the problem of lengthy, time-consuming inspections, a terminal block component detection and identification method is presented in this paper. The identification method is a multi-stage system that incorporates a streamlined version of You Only Look Once version 7 (YOLOv7), a fusion of YOLOv7 and differential binarization (DB), and the utilization of PaddleOCR. Firstly, the YOLOv7 Area-Oriented (YOLOv7-AO) model is developed to precisely locate the complete region of terminal blocks within substation scene images. The compact area extraction model rapidly cuts out the valid proportion of the input image. Furthermore, the DB segmentation head is integrated into the YOLOv7 model to effectively handle the densely arranged, irregularly shaped block components. To detect all the components within a target electrical cabinet of substation equipment, the YOLOv7 model with a differential binarization attention head (YOLOv7-DBAH) is proposed, integrating spatial and channel attention mechanisms. Finally, a general OCR algorithm is applied to the cropped-out instances after image distortion to match and record the component’s identity information. The experimental results show that the YOLOv7-AO model reaches high detection accuracy with good portability, gaining 4.45 times faster running speed. Moreover, the terminal block component detection results show that the YOLOv7-DBAH model achieves the highest evaluation metrics, increasing the F1-score from 0.83 to 0.89 and boosting the precision to over 0.91. The proposed method achieves the goal of terminal block component identification and can be applied in practical situations.

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A Segmentation-Based Multitask Learning Approach for Isolating Switch State Recognition in High-Speed Railway Traction Substation

Cited by 11 publications

References 41 publications

A Semantically Annotated 15-Class Ground Truth Dataset for Substation Equipment to Train Semantic Segmentation Models

A Semantically Annotated 15-Class Ground Truth Dataset for Substation Equipment to Train Semantic Segmentation Models

Accurate identification and measurement of the precipitate area by two-stage deep neural networks in novel chromium-based alloys

An Identification Method for Irregular Components Related to Terminal Blocks in Equipment Cabinet of Power Substation

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