Defect Recognition Method Based on HOG and SVM for Drone Inspection Images of Power Transmission Line

Mao, Tianqi; Ren, Lingran; Faqiang, Yuan,; Li, Chenxiao; Zhang, Li; Zhang, Min; Chen, Yu

doi:10.1109/hpbdis.2019.8735466

Cited by 26 publications

(14 citation statements)

References 7 publications

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“…In this paper, we propose an interdomain heterogeneous transfer learning method combining CNN and transfer learning for the problem of defect recognition in weld inspection images. e effect [21] of freezing different [11] convolutional layers on the feature expression ability of the image and the [6] recognition performance of the model is investigated, and the final results show that the interdomain heterogeneous transfer learning method based on convolutional neural network not only effectively overcomes the problem of small amount of data but also effectively improves the recognition performance and generalization ability of the model, increases the average recognition accuracy of the model to more than 90%, and can effectively achieve the [22] defect recognition of weld inspection image task.…”

Section: Discussionmentioning

confidence: 99%

“…With the continuous research, the methods for automatic detection and recognition of weld defects are being expanded, but from the results of the existing literature studies, the methods used in the process of weld defect recognition are mainly divided into a three-stage processing process, which are image segmentation, feature extraction, and defect classification [10]. Mao et al [11] used median filtering technique to remove the noise in the image, followed by image enhancement technique, Ostu image segmentation method, edge detection technique, and Hough transform to calculate the region of interest of the X-ray image, and obtained a better segmentation effect without manually designing a suitable segmentation threshold. Abd El-aziz et al [12] proposed an improved Ostu algorithm weighted object variance (WOV), which ensures that the threshold value is always the value located at the valley of two peaks or at the lower left edge of a single peak histogram, solving the problem of threshold selection in histograms in the case of single and double peaks, and the results show that the WOV algorithm outperforms Ostu, maximum entropy, valley-emphasis, and other algorithms.…”

Section: Related Workmentioning

confidence: 99%

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Image Defect Recognition Method Based on Deep Learning Network

2022

Mobile Information Systems

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Deep learning techniques are used to identify weld image defects in the process of image defect recognition. In this paper, a transfer learning method based on convolutional neural networks is proposed for the recognition problem of deep neural network models on weld flaw detection image data sets. Designing interdomain heterogeneous transfer learning with the pretrained model on the large data set, the interdomain heterogeneous transfer learning is used to transfer the pretrained model in the source data domain to the weld inspection image data set according to the difference of the content in the source and target data domains, and the effectiveness of the transfer learning in weld inspection image defect recognition is verified by fine-tuning the whole network by training the parameters of different layers using the frozen layer method. The effect of freezing different layers on the recognition performance of the model is also investigated.

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Section: Discussionmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Image Defect Recognition Method Based on Deep Learning Network

2022

Mobile Information Systems

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“…If a feature vector lies outside the hypersphere found by SVDD during testing, the image patch corresponding to this feature vector is considered anomalous. Similar works can be found in [18][19][20]. Compared to these traditional dimensionality reduction models, a convolutional neural network (CNN) provides nonlinear mapping and is better at extracting semantic information.…”

Section: Feature Extraction Based Methodsmentioning

confidence: 82%

Image Anomaly Detection Using Normal Data Only by Latent Space Resampling

et al. 2020

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Detecting image anomalies automatically in industrial scenarios can improve economic efficiency, but the scarcity of anomalous samples increases the challenge of the task. Recently, autoencoder has been widely used in image anomaly detection without using anomalous images during training. However, it is hard to determine the proper dimensionality of the latent space, and it often leads to unwanted reconstructions of the anomalous parts. To solve this problem, we propose a novel method based on the autoencoder. In this method, the latent space of the autoencoder is estimated using a discrete probability model. With the estimated probability model, the anomalous components in the latent space can be well excluded and undesirable reconstruction of the anomalous parts can be avoided. Specifically, we first adopt VQ-VAE as the reconstruction model to get a discrete latent space of normal samples. Then, PixelSail, a deep autoregressive model, is used to estimate the probability model of the discrete latent space. In the detection stage, the autoregressive model will determine the parts that deviate from the normal distribution in the input latent space. Then, the deviation code will be resampled from the normal distribution and decoded to yield a restored image, which is closest to the anomaly input. The anomaly is then detected by comparing the difference between the restored image and the anomaly image. Our proposed method is evaluated on the high-resolution industrial inspection image datasets MVTec AD which consist of 15 categories. The results show that the AUROC of the model improves by 15% over autoencoder and also yields competitive performance compared with state-of-the-art methods.

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“…Other methods for the automated identi cation and monitoring of the electric power transmission network (EPTN) faults have been implemented in recent years using supervised classi cation. The most used machine learningbased algorithms used as the feature classi er, primarily include Adaboost [28], and SVM [29], which have been applied successfully to detect foreign bodies on the conductor. These techniques have contributed to improve the accuracy of electrical transmission fault inspection.…”

Section: Related Workmentioning

confidence: 99%

Deep Learning for Component Fault Detection in Electricity Transmission Lines.

Maduako¹,

Igwe²,

Abah³

et al. 2021

Preprint

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Fault identification is one of the most significant bottlenecks faced by electricity transmission and distribution utilities in developing countries to deliver efficient services to the customers and ensure proper asset audit and management for network optimization and load forecasting. This is due to data scarcity, asset inaccessibility and insecurity, ground-surveys complexity, untimeliness, and general human cost. In view of this, we exploited the use of oblique UAV imagery with a high spatial resolution and a fine-tuned and deep Convolutional Neural Networks (CNNs) to monitor four major Electric power transmission network (EPTN) components. This study explored the capability of the Single Shot Multibox Detector (SSD), a one-stage object detection model on the electric transmission power line imagery to localize, detect and classify faults. The fault considered in this study include the broken insulator plate, missing insulator plate, missing knob, and rusty clamp. Our adapted neural network is a CNN based on a multiscale layer feature pyramid network (FPN) using aerial image patches and ground truth to localise and detect faults via a one-phase procedure. The SSD Rest50 architecture variation performed the best with a mean Average Precision (mAP) of 89.61%. All the developed SSD based models achieve a high precision rate and low recall rate in detecting the faulty components, thus achieving acceptable balance levels of F1-score and representation. Finally, comparable to other works in literature within this same domain, deep-learning will boost timeliness of EPTN inspection and their component fault mapping in the long - run if these deep learning architectures are widely understood, adequate training samples exist to represent multiple fault characteristics; and the effects of augmenting available datasets, balancing intra-class heterogeneity, and small-scale datasets are clearly understood.

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Defect Recognition Method Based on HOG and SVM for Drone Inspection Images of Power Transmission Line

Cited by 26 publications

References 7 publications

Image Defect Recognition Method Based on Deep Learning Network

Image Defect Recognition Method Based on Deep Learning Network

Image Anomaly Detection Using Normal Data Only by Latent Space Resampling

Deep Learning for Component Fault Detection in Electricity Transmission Lines.

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