Detection Method of Insulator Based on Faster R-CNN

Ma, Lei; Xu, Chen; Zuo, Guoyu; Bo, Bing; Tao, Fengbo

doi:10.1109/cyber.2017.8446155

Cited by 21 publications

(8 citation statements)

References 7 publications

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“…The additional convolution layers shown in Figure 2.4 again and represented by arrows coming out of the layers transmit us to the NMS layer where the object to be detected is and where it is decided. At the end of this process, the decision emerges (Ma et al 2018).…”

Section: Methods Used For Mask Detectionmentioning

confidence: 99%

Deep Learning Based Mask Detection in Smart Home Entries During the Epidemic Process

Cerit

Bayır

2020

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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Abstract. In this study, "smart home" systems were designed against Covid-19 virus, which negatively affects life all over the world, and viruses that may become epidemics later. Our homes need to be more hygienic and safe than yesterday. One of these hygiene rules is the masks that cover our nose and mouth. It is very important to use a mask to prevent further spread of the virus. Whether or not the people in smart homes are wearing masks at home will be diagnosed with the deep learning method. Hosts will be warned if they do not have masks. Brightness level control card and illuminator have been added to smart home entrances to better identify people's faces. With PID, the illumination level is fixed at the desired value, and with IOT technology, people can follow the illumination level at the smart home entrance from the mobile application.

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Section: Methods Used For Mask Detectionmentioning

confidence: 99%

Deep Learning Based Mask Detection in Smart Home Entries During the Epidemic Process

Cerit

Bayır

2020

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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“…The two-stage methods have two common shortcomings: that they are time-consuming and hard to train. On the contrary, the one-stage methods can run in real-time at a moderate expense of accuracy compared with the two-stage methods [47][48][49]. Therefore, one-stage methods have higher feasibility for deployment on embedded devices.…”

Section: Appl Sci 2019 9 X For Peer Review 2 Of 22mentioning

confidence: 99%

“…Motivated by these pioneering researches, it is worth investigating how to use deep learning models to locate insulators and detect faults in aerial images [1]. Although there are few related works, a summary of literatures are given and analyzed as follows: In the work of [40,41,48,49], Fast-RCNN and Faster-RCNN are adopted to locate the insulators. However, the training process of Fast-RCNN and Faster-RCNN is complicated and difficult to deploy.…”

Section: Appl Sci 2019 9 X For Peer Review 2 Of 22mentioning

confidence: 99%

A Method of Insulator Faults Detection in Aerial Images for High-Voltage Transmission Lines Inspection

et al. 2019

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Insulator faults detection is an important task for high-voltage transmission line inspection. However, current methods often suffer from the lack of accuracy and robustness. Moreover, these methods can only detect one fault in the insulator string, but cannot detect a multi-fault. In this paper, a novel method is proposed for insulator one fault and multi-fault detection in UAV-based aerial images, the backgrounds of which usually contain much complex interference. The shapes of the insulators also vary obviously due to the changes in filming angle and distance. To reduce the impact of complex interference on insulator faults detection, we make full use of the deep neural network to distinguish between insulators and background interference. First of all, plenty of insulator aerial images with manually labelled ground-truth are collected to construct a standard insulator detection dataset ‘InST_detection’. Secondly, a new convolutional network is proposed to obtain accurate insulator string positions in the aerial image. Finally, a novel fault detection method is proposed that can detect both insulator one fault and multi-fault in aerial images. Experimental results on a large number of aerial images show that our proposed method is more effective and efficient than the state-of-the-art insulator fault detection methods.

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“…Recently, with increasing attention paid to the deep neural networks, researchers have devoted great efforts to the development of deep learning-based insulator string detection methods. Specifically, the strategies of these methods can be attributed to two categories: object detection based [29][30][31][32] and semantic segmentation based [33][34][35][36]. Although these methods achieve good performances when compared with the previous works, they have to use data augmentation methods, such as rotation and flipping, to expand datasets to meet the training needs of deep neural networks.…”

Section: Introductionmentioning

confidence: 99%

Search Like an Eagle: A Cascaded Model for Insulator Missing Faults Detection in Aerial Images

Han

Yang

et al. 2020

Energies

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Insulator missing fault is a serious accident of high-voltage transmission lines, which can cause abnormal energy supply. Recently, a lot of vision-based methods are proposed for detecting an insulator missing fault in aerial images. However, these methods usually lack efficiency and robustness due to the effect of the complex background interferences in the aerial images. More importantly, most of these methods cannot address the insulator multi-fault detection. This paper proposes an unprecedented cascaded model to detect insulator multi-fault in the aerial images to solve the existing challenges. Firstly, a total of 764 images are adopted to create a novel insulator missing faults dataset ‘IMF-detection’. Secondly, a new network is proposed to locate the insulator string from the complex background. Then, the located region that contains the insulator string is set to be an RoI (region of interest) region. Finally, the YOLO-v3 tiny network is trained and then used to detect the insulator missing faults in the RoI region. Experimental results and analysis validate that the proposed method is more efficient and robust than some previous works. Most importantly, the average running time of the proposed method is about 30ms, which demonstrates that it has the potential to be adopted for the on-line detection of insulator missing faults.

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Detection Method of Insulator Based on Faster R-CNN

Cited by 21 publications

References 7 publications

Deep Learning Based Mask Detection in Smart Home Entries During the Epidemic Process

Deep Learning Based Mask Detection in Smart Home Entries During the Epidemic Process

A Method of Insulator Faults Detection in Aerial Images for High-Voltage Transmission Lines Inspection

Search Like an Eagle: A Cascaded Model for Insulator Missing Faults Detection in Aerial Images

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