Deep Learning Approaches on Defect Detection in High Resolution Aerial Images of Insulators

Wen, Qiaodi; Luo, Ziqi; Chen, Ruitao; Yang, Yifan; Li, Guofa

doi:10.3390/s21041033

Cited by 62 publications

(28 citation statements)

References 44 publications

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“…According to Wen et al [48], the use of a small dataset is a challenge in this analysis. This is mainly because system failures are seldom found during inspections since, when a failure occurs, the electrical utility replaces the components immediately.…”

Section: Related Work and Considered Datasetmentioning

confidence: 99%

Semi-ProtoPNet Deep Neural Network for the Classification of Defective Power Grid Distribution Structures

Stefenon

Singh

Yow

et al. 2022

Sensors

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Power distribution grids are typically installed outdoors and are exposed to environmental conditions. When contamination accumulates in the structures of the network, there may be shutdowns caused by electrical arcs. To improve the reliability of the network, visual inspections of the electrical power system can be carried out; these inspections can be automated using computer vision techniques based on deep neural networks. Based on this need, this paper proposes the Semi-ProtoPNet deep learning model to classify defective structures in the power distribution networks. The Semi-ProtoPNet deep neural network does not perform convex optimization of its last dense layer to maintain the impact of the negative reasoning process on image classification. The negative reasoning process rejects the incorrect classes of an input image; for this reason, it is possible to carry out an analysis with a low number of images that have different backgrounds, which is one of the challenges of this type of analysis. Semi-ProtoPNet achieves an accuracy of 97.22%, being superior to VGG-13, VGG-16, VGG-19, ResNet-34, ResNet-50, ResNet-152, DenseNet-121, DenseNet-161, DenseNet-201, and also models of the same class such as ProtoPNet, NP-ProtoPNet, Gen-ProtoPNet, and Ps-ProtoPNet.

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Section: Related Work and Considered Datasetmentioning

confidence: 99%

Semi-ProtoPNet Deep Neural Network for the Classification of Defective Power Grid Distribution Structures

Stefenon

Singh

Yow

et al. 2022

Sensors

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“…To enhance the reuse and spread of insulator features, Liu et al [22] added a multi-level feature mapping module based on YOLOv3 and Dense Blocks and proposed an insulator detection network called YOLOv3-dense. Focusing on the interference of complex backgrounds and small targets, the researchers in [23] proposed two insulator detection models, Exact R-CNN and CME-CNN. CME-CNN added an encoder-decoder based on Exact R-CNN to obtain pure insulators.…”

Section: Insulator Detectionmentioning

confidence: 99%

InsulatorGAN: A Transmission Line Insulator Detection Model Using Multi-Granularity Conditional Generative Adversarial Nets for UAV Inspection

Chen

Zhao

2021

Remote Sensing

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Insulator detection is one of the most significant issues in high-voltage transmission line inspection using unmanned aerial vehicles (UAVs) and has attracted attention from researchers all over the world. The state-of-the-art models in object detection perform well in insulator detection, but the precision is limited by the scale of the dataset and parameters. Recently, the Generative Adversarial Network (GAN) was found to offer excellent image generation. Therefore, we propose a novel model called InsulatorGAN based on using conditional GANs to detect insulators in transmission lines. However, due to the fixed categories in datasets such as ImageNet and Pascal VOC, the generated insulator images are of a low resolution and are not sufficiently realistic. To solve these problems, we established an insulator dataset called InsuGenSet for model training. InsulatorGAN can generate high-resolution, realistic-looking insulator-detection images that can be used for data expansion. Moreover, InsulatorGAN can be easily adapted to other power equipment inspection tasks and scenarios using one generator and multiple discriminators. To give the generated images richer details, we also introduced a penalty mechanism based on a Monte Carlo search in InsulatorGAN. In addition, we proposed a multi-scale discriminator structure based on a multi-task learning mechanism to improve the quality of the generated images. Finally, experiments on the InsuGenSet and CPLID datasets demonstrated that our model outperforms existing state-of-the-art models by advancing both the resolution and quality of the generated images as well as the position of the detection box in the images.

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“…The SSD-based mode could achieve automatic multi-level feature extraction to identify aerial images with complex backgrounds [12]. Exact region-based convolutional neural network (Exact R-CNN) and cascade mask extraction and exact region-based convolutional neural network (CME-CNN) were proposed in the literature [13]. These methods could solve the accuracies of existing detection methods limited by the complex background interference and small target detection.…”

Section: Introductionmentioning

confidence: 99%

A Novel Auto-Synthesis Dataset Approach for Fitting Recognition Using Prior Series Data

Zhang

Qin

Jin

et al. 2022

Sensors

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To address power transmission line (PTL) traversing complex environments leading to data collection being difficult and costly, we propose a novel auto-synthesis dataset approach for fitting recognition using prior series data. The approach mainly includes three steps: (1) formulates synthesis rules by the prior series data; (2) renders 2D images based on the synthesis rules utilizing advanced virtual 3D techniques; (3) generates the synthetic dataset with images and annotations obtained by processing images using the OpenCV. The trained model using the synthetic dataset was tested by the real dataset (including images and annotations) with a mean average precision (mAP) of 0.98, verifying the feasibility and effectiveness of the proposed approach. The recognition accuracy by the test is comparable with training by real samples and the cost is greatly reduced to generate synthetic datasets. The proposed approach improves the efficiency of establishing a dataset, providing a training data basis for deep learning (DL) of fitting recognition.

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Deep Learning Approaches on Defect Detection in High Resolution Aerial Images of Insulators

Cited by 62 publications

References 44 publications

Semi-ProtoPNet Deep Neural Network for the Classification of Defective Power Grid Distribution Structures

Semi-ProtoPNet Deep Neural Network for the Classification of Defective Power Grid Distribution Structures

InsulatorGAN: A Transmission Line Insulator Detection Model Using Multi-Granularity Conditional Generative Adversarial Nets for UAV Inspection

A Novel Auto-Synthesis Dataset Approach for Fitting Recognition Using Prior Series Data

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