Porcelain Insulator Crack Location and Surface States Pattern Recognition Based on Hyperspectral Technology

Zhao, Yiming; Jing, Yan; Wang, Yanxin; Jing, Qianzhen; Liu, Tingliang

doi:10.3390/e23040486

Cited by 8 publications

(4 citation statements)

References 33 publications

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“…Regular maintenance contributes to effectively extending the service life of these infrastructures [1][2][3]. Automatic crack detection based on computer vision has become an instantly efficient and widely adopted method due to its non-contact and cost-effectiveness [4][5][6][7]. This task is essentially treated as a binary image segmentation problem, where each pixel in an image is classified as either "crack" or "non-crack".…”

Section: Introductionmentioning

confidence: 99%

CAC: Confidence-Aware Co-Training for Weakly Supervised Crack Segmentation

Liang,

Li,

et al. 2024

Entropy

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Automatic crack segmentation plays an essential role in maintaining the structural health of buildings and infrastructure. Despite the success in fully supervised crack segmentation, the costly pixel-level annotation restricts its application, leading to increased exploration in weakly supervised crack segmentation (WSCS). However, WSCS methods inevitably bring in noisy pseudo-labels, which results in large fluctuations. To address this problem, we propose a novel confidence-aware co-training (CAC) framework for WSCS. This framework aims to iteratively refine pseudo-labels, facilitating the learning of a more robust segmentation model. Specifically, a co-training mechanism is designed and constructs two collaborative networks to learn uncertain crack pixels, from easy to hard. Moreover, the dynamic division strategy is designed to divide the pseudo-labels based on the crack confidence score. Among them, the high-confidence pseudo-labels are utilized to optimize the initialization parameters for the collaborative network, while low-confidence pseudo-labels enrich the diversity of crack samples. Extensive experiments conducted on the Crack500, DeepCrack, and CFD datasets demonstrate that the proposed CAC significantly outperforms other WSCS methods.

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Section: Introductionmentioning

confidence: 99%

CAC: Confidence-Aware Co-Training for Weakly Supervised Crack Segmentation

Liang,

Li,

et al. 2024

Entropy

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“…However, due to the breakdown characteristics of ceramic insulators, their performance gradually deteriorates under long-term effects of electric field, mechanical stress, and climate change, which poses a safety hazard to the operation of the power grid [1]. Therefore, detecting and identifying ceramic insulators is crucial for ensuring the safe operation of the power grid [2]. Traditional manual inspection of power systems has problems such as large workload and poor detection results.…”

Section: Introductionmentioning

confidence: 99%

The automatic recognition algorithm for surface defects of ceramic insulators on transmission towers based on deformable U-Net network

Huang,

Cai,

Zhou

et al. 2023

Third International Conference on Computer Graphics, Image, and Virtualization (ICCGIV 2023)

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To address the issues of low efficiency, insufficient accuracy, and high miss rate in traditional inspection methods for surface defects on ceramic insulators of transmission towers, this paper introduces a UAV-based intelligent inspection solution based on the deformable U-Net network to effectively detect and recognize surface defects on ceramic insulators in transmission towers. By using the deformable convolution operator to optimize the U-net network's convolution layer, the perceptual range of the convolution kernel is extended to improve the integrity of defect detail information. Meanwhile, the full-scale skip connection model is used to integrate high-dimensional and low-dimensional feature information to further improve the accuracy of ceramic insulator surface defect feature recognition. The experimental results show that the UAV-based intelligent inspection solution based on the deformable U-Net network can achieve an identification accuracy of 97.5%, an average precision of 95.55%, and an average intersection over union (IOU) of 91.67% in ceramic insulator surface defect detection. Compared with the traditional U-net method, the proposed solution in this study has improved the ceramic insulator surface defect inspection accuracy by 7.6%.

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“…Insulators, as special insulating devices in the transmission lines, are required to withstand the power load and mechanical loads brought by the transmission lines when they work [1]. As a result of long-term exposure to the natural environment, but also subject to dirt, lightning, strong winds, and bird damage, as well as other external factors, these factors will make the insulators on the transmission line gradually age and break [2]. In the operation of transmission lines, defects of insulators often include zero value, broken string, corrosion, etc.…”

Section: Introductionmentioning

confidence: 99%

High-Accuracy Insulator Defect Detection for Overhead Transmission Lines Based on Improved YOLOv5

Huang

Jiang

et al. 2022

Applied Sciences

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As a key component in overhead cables, insulators play an important role. However, in the process of insulator inspection, due to background interference, small fault area, limitations of manual detection, and other factors, detection is difficult, has low accuracy, and is prone to missed detection and false detection. To detect insulator defects more accurately, the insulator defect detection algorithm based on You Only Look Once version 5 (YOLOv5) is proposed. A backbone network was built with lightweight modules to reduce network computing overhead. The small-scale network detection layer was increased to improve the network for small target detection accuracy. A receptive field module was designed to replace the original spatial pyramid pooling (SPP) module so that the network can obtain feature information and improve network performance. Finally, experiments were carried out on the insulator image dataset. The experimental results show that the average accuracy of the algorithm is 97.4%, which is 7% higher than that of the original YOLOv5 network, and the detection speed is increased by 10 fps, which improves the accuracy and speed of insulator detection.

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Porcelain Insulator Crack Location and Surface States Pattern Recognition Based on Hyperspectral Technology

Cited by 8 publications

References 33 publications

CAC: Confidence-Aware Co-Training for Weakly Supervised Crack Segmentation

CAC: Confidence-Aware Co-Training for Weakly Supervised Crack Segmentation

The automatic recognition algorithm for surface defects of ceramic insulators on transmission towers based on deformable U-Net network

High-Accuracy Insulator Defect Detection for Overhead Transmission Lines Based on Improved YOLOv5

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