SRGAN based super-resolution reconstruction of power inspection images

• Published in: Discover applied sciences 2024-11, Vol.6 (12), p.639-16, Article 639
• Main Authors: Zhou, Jianjun, Zhang, Jianbo, Jia, Jiangang, Liu, Jie
• Format: Article
• Language: English
• Subjects: Accuracy; Algorithms; Applied and Technical Physics; BDZ dataset; Brief Communication; Cameras; Chemistry/Food Science; Datasets; Deep learning; Design; Drones; Earth Sciences; Efficiency; Electric equipment; Electric power; Electric power systems; Electrical equipment; Engineering; Environment; Fault detection; Generative adversarial networks; High resolution; Image enhancement; Image processing; Image reconstruction; Image resolution; Inspection; Inspections; Machine learning; Materials Science; Medical imaging; Neural networks; Power inspections; Power lines; Super-resolution reconstruction; Unmanned aerial vehicles
• ISSN: 3004-9261; 2523-3963; 3004-9261; 2523-3971
• DOI: 10.1007/s42452-024-06350-x

Ensuring the operational safety of the electric power system critically depends on effective power inspections. However, traditional methods face challenges in detecting minor faults such as cracks and corrosion in electrical equipment. In this thesis, the Super Resolution Generative Adversarial Network (SRGAN) is introduced into the field of power inspection for the first time. Additionally, the dedicated dataset (BDZ dataset) was developed. This includes a large number of high-resolution images for power line inspection. The primary objective is to enhance the resolution of inspection images, thereby significantly improving the accuracy and reliability of defect detection in the power system. Numerous experiments have demonstrated that the SRGAN model outperforms traditional models in the super-resolution reconstruction of power inspection images, particularly in recovering image texture details. Using the BDZ dataset significantly enhances image resolution. When employing the same SRGAN model, PSNR increased by 2.47 dB and SSIM by 4.10% compared to the standard dataset. This research introduces new methodologies for advancing electric power inspection technologies, providing a more robust assurance for the safe and reliable operation of electric power systems. Article Highlights SRGAN-based image reconstruction algorithm for power inspection, providing a new solution for traditional inspection. We developed the BDZ dataset for power inspections, which contains high-resolution images of 1000 power devices. Our approach has wide applications in the field of power detection. It improves the accuracy of detection and reduces the cost.