Missing-Sheds Granularity Estimation of Glass Insulators Using Deep Neural Networks Based on Optical Imaging

Chen, Wenxiang; Li, Yingna; Zhao, Zhengang

doi:10.3390/s22051737

Cited by 9 publications

(6 citation statements)

References 45 publications

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“…Hence, there is still much room for improvement in this work [ 40 ]. Compared with the current automation level of ceramic defect inspection in Chen et al, the results of this work have been improved [ 41 ]. By adding DL, the intellectualization of ceramic defect inspection technology has been improved.…”

Section: Discussionmentioning

confidence: 99%

Inspecting Decorative Ceramic Defects by Fusing Convolutional Neural Network and Image Recognition

Jin

Wang

2022

Computational Intelligence and Neuroscience

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The intelligent inspection of ceramic decorative defects is one of the hot research at present. This work aims to improve the defect inspection automation of finished decorative ceramic workpieces. First, it introduces the multi-target detection algorithm and compares the performance of different network models on the public data set. Second, the initial images are collected on the spot. The initial pictures are easy to produce noise in actual deployment, affecting the image quality. Therefore, image preprocessing is performed for the initial images, and a median filtering method is used to calculate the denoising. Finally, the original You Only Look Once version 3 network model is realized. Based on this, the decorative ceramic-oriented Automated Surface Defect Inspection model is proposed. Then, decorative ceramic defect images are inputted for model training. The experimental conclusions are deeply studied and analyzed. The results show that the proposed decorative ceramic-oriented Automated Surface Defect Inspection model based on Deep Learning technology has good feature extraction and inspection ability. The detection accuracy is 94.90% on the test set, and the detection speed reaches 25 frames per second. Compared with the traditional manual inspection method, the proposed model greatly improves the inspection effect and can meet the on-site inspection requirements of surface defects of decorative ceramics under complex backgrounds. It is of great significance to improve the quality inspection efficiency and economic benefits of China’s decorative ceramics industry.

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Section: Discussionmentioning

confidence: 99%

Inspecting Decorative Ceramic Defects by Fusing Convolutional Neural Network and Image Recognition

Jin

Wang

2022

Computational Intelligence and Neuroscience

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“…However, the number of network model parameters was large, and the training and reasoning time of the model was long; thus, the detection speed was still unsatisfactory. Chen et al used an INSU-YOLO model combined with an attention mechanism to detect insulator defects, which improved the low recognition rate of small target defects [13]. However, the data set was only limited to glass insulators, and no further study was conducted on other types of insulators.…”

Section: Introductionmentioning

confidence: 99%

“…Chen et al . used an INSU‐YOLO model combined with an attention mechanism to detect insulator defects, which improved the low recognition rate of small target defects [13]. However, the data set was only limited to glass insulators, and no further study was conducted on other types of insulators.…”

Section: Introductionmentioning

confidence: 99%

Defect Identification of Power Line Insulators Based on a MobileViT‐Yolo Deep Learning Algorithm

Weidong

Dong

Zhang

et al. 2023

IEEJ Transactions Elec Engng

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Power line insulator defect identification usually suffers from complex backgrounds, small defect target sizes, and inconspicuous defect features. Traditional identification methods based on image processing, image analysis, and pattern classification have many limitations in solving the aforementioned problems. In recent decades, deep learning classification methods have gradually replaced traditional identification methods in the task of power line insulator defect identification. To accurately identify the locations of insulator defects, this paper proposes an insulator defect detection algorithm using an improved lightweight YOLOv4‐tiny network (ILYTN). First, the CBL (Conv‐BN‐LeakyReLU) modules of the backbone network are replaced with MobileViT blocks to enhance the feature extraction capability of the backbone network. Second, coordinate attention (CA) is introduced in the feature fusion part to improve the network's ability to focus on the location of defects. Finally, an EIOU (efficient intersection over union) loss function, instead of the original CIOU loss function, is used so that the convergence speed of the network can be improved. To verify the effectiveness of the proposed algorithm in this paper, the mViT‐yolo algorithm is compared with the mainstream Faster‐RCNN algorithm, SSD algorithm, YOLOv3 algorithm, and YOLOv4‐tiny algorithm. The experimental results show that the algorithm proposed in this paper outperforms all the above algorithms in terms of detection accuracy. Compared with the traditional YOLOv4‐tiny algorithm, the proposed algorithm increases the mean average precision (mAP) by 1.64%, the average precision (AP) of missing insulator defects by 0.32%, and the average precision (AP) of broken insulator defects by 4.96%. © 2023 The Authors. IEEJ Transactions on Electrical and Electronic Engineering published by Institute of Electrical Engineer of Japan and Wiley Periodicals LLC.

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“…In [24], an end-to-end YOLO network model is used and a more accurate position of component defects in the transmission line is obtained by adding a coordinate attention module. In [25], the features of insulators with different specifications were extracted based on a deep neural network. The INSU-YOLO detection method was proposed, and the insulator defect dataset was constructed to avoid the problem of network overfitting caused by insufficient data.…”

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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Missing-Sheds Granularity Estimation of Glass Insulators Using Deep Neural Networks Based on Optical Imaging

Cited by 9 publications

References 45 publications

Inspecting Decorative Ceramic Defects by Fusing Convolutional Neural Network and Image Recognition

Inspecting Decorative Ceramic Defects by Fusing Convolutional Neural Network and Image Recognition

Defect Identification of Power Line Insulators Based on a MobileViT‐Yolo Deep Learning Algorithm

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

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