UAV Aerial Image Generation of Crucial Components of High-Voltage Transmission Lines Based on Multi-Level Generative Adversarial Network
With the aim of improving the image quality of the crucial components of transmission lines taken by unmanned aerial vehicles (UAV), a priori work on the defective fault location of high-voltage transmission lines has attracted great attention from researchers in the UAV field. In recent years, gene...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-03-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/15/5/1412 |
| _version_ | 1797614455285088256 |
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| author | Jinyu Wang Yingna Li Wenxiang Chen |
| author_facet | Jinyu Wang Yingna Li Wenxiang Chen |
| author_sort | Jinyu Wang |
| collection | DOAJ |
| description | With the aim of improving the image quality of the crucial components of transmission lines taken by unmanned aerial vehicles (UAV), a priori work on the defective fault location of high-voltage transmission lines has attracted great attention from researchers in the UAV field. In recent years, generative adversarial nets (GAN) have achieved good results in image generation tasks. However, the generation of high-resolution images with rich semantic details from complex backgrounds is still challenging. Therefore, we propose a novel GANs-based image generation model to be used for the critical components of power lines. However, to solve the problems related to image backgrounds in public data sets, considering that the image background of the common data set CPLID (Chinese Power Line Insulator Dataset) is simple. However, it cannot fully reflect the complex environments of transmission line images; therefore, we established an image data set named “KCIGD” (The Key Component Image Generation Dataset), which can be used for model training. CFM-GAN (GAN networks based on coarse–fine-grained generators and multiscale discriminators) can generate the images of the critical components of transmission lines with rich semantic details and high resolutions. CFM-GAN can provide high-quality image inputs for transmission line fault detection and line inspection models to guarantee the safe operation of power systems. Additionally, we can use these high-quality images to expand the data set. In addition, CFM-GAN consists of two generators and multiple discriminators, which can be flexibly applied to image generation tasks in other scenarios. We introduce a penalty mechanism-related Monte Carlo search (MCS) approach in the CFM-GAN model to introduce more semantic details in the generated images. Moreover, we presented a multiscale discriminator structure according to the multitask learning mechanisms to effectively enhance the quality of the generated images. Eventually, the experiments using the CFM-GAN model on the KCIGD dataset and the publicly available CPLID indicated that the model used in this work outperformed existing mainstream models in improving image resolution and quality. |
| first_indexed | 2024-03-11T07:11:39Z |
| format | Article |
| id | doaj.art-9171ee82948d451bac11e572b1eae867 |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-11T07:11:39Z |
| publishDate | 2023-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-9171ee82948d451bac11e572b1eae8672023-11-17T08:32:46ZengMDPI AGRemote Sensing2072-42922023-03-01155141210.3390/rs15051412UAV Aerial Image Generation of Crucial Components of High-Voltage Transmission Lines Based on Multi-Level Generative Adversarial NetworkJinyu Wang0Yingna Li1Wenxiang Chen2Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming 650500, ChinaFaculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming 650500, ChinaFaculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming 650500, ChinaWith the aim of improving the image quality of the crucial components of transmission lines taken by unmanned aerial vehicles (UAV), a priori work on the defective fault location of high-voltage transmission lines has attracted great attention from researchers in the UAV field. In recent years, generative adversarial nets (GAN) have achieved good results in image generation tasks. However, the generation of high-resolution images with rich semantic details from complex backgrounds is still challenging. Therefore, we propose a novel GANs-based image generation model to be used for the critical components of power lines. However, to solve the problems related to image backgrounds in public data sets, considering that the image background of the common data set CPLID (Chinese Power Line Insulator Dataset) is simple. However, it cannot fully reflect the complex environments of transmission line images; therefore, we established an image data set named “KCIGD” (The Key Component Image Generation Dataset), which can be used for model training. CFM-GAN (GAN networks based on coarse–fine-grained generators and multiscale discriminators) can generate the images of the critical components of transmission lines with rich semantic details and high resolutions. CFM-GAN can provide high-quality image inputs for transmission line fault detection and line inspection models to guarantee the safe operation of power systems. Additionally, we can use these high-quality images to expand the data set. In addition, CFM-GAN consists of two generators and multiple discriminators, which can be flexibly applied to image generation tasks in other scenarios. We introduce a penalty mechanism-related Monte Carlo search (MCS) approach in the CFM-GAN model to introduce more semantic details in the generated images. Moreover, we presented a multiscale discriminator structure according to the multitask learning mechanisms to effectively enhance the quality of the generated images. Eventually, the experiments using the CFM-GAN model on the KCIGD dataset and the publicly available CPLID indicated that the model used in this work outperformed existing mainstream models in improving image resolution and quality.https://www.mdpi.com/2072-4292/15/5/1412unmanned aerial vehicle (UAV)high-voltage transmission line inspectiongenerative adversarial netsimage generation |
| spellingShingle | Jinyu Wang Yingna Li Wenxiang Chen UAV Aerial Image Generation of Crucial Components of High-Voltage Transmission Lines Based on Multi-Level Generative Adversarial Network Remote Sensing unmanned aerial vehicle (UAV) high-voltage transmission line inspection generative adversarial nets image generation |
| title | UAV Aerial Image Generation of Crucial Components of High-Voltage Transmission Lines Based on Multi-Level Generative Adversarial Network |
| title_full | UAV Aerial Image Generation of Crucial Components of High-Voltage Transmission Lines Based on Multi-Level Generative Adversarial Network |
| title_fullStr | UAV Aerial Image Generation of Crucial Components of High-Voltage Transmission Lines Based on Multi-Level Generative Adversarial Network |
| title_full_unstemmed | UAV Aerial Image Generation of Crucial Components of High-Voltage Transmission Lines Based on Multi-Level Generative Adversarial Network |
| title_short | UAV Aerial Image Generation of Crucial Components of High-Voltage Transmission Lines Based on Multi-Level Generative Adversarial Network |
| title_sort | uav aerial image generation of crucial components of high voltage transmission lines based on multi level generative adversarial network |
| topic | unmanned aerial vehicle (UAV) high-voltage transmission line inspection generative adversarial nets image generation |
| url | https://www.mdpi.com/2072-4292/15/5/1412 |
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