Research on Denoising and Defect Recognition of Visible Light Images of Power Equipment Based on Neural Network

The ongoing expansion of power systems has led to higher demands for equipment reliability. Identifying and addressing faults in power equipment at an early stage is essential for minimizing losses and preventing accidents. Traditional manual inspection approaches are often inefficient and present safety concerns, particularly as the scale and complexity of power infrastructure continue to grow. This paper introduces a neural network-based approach for denoising and defect detection in visible light images of power equipment. The proposed method comprises three key components: image denoising using a deep residual network, defect identification via a transfer learning-based neural network, and the development of a graphical user interface (GUI) toolkit for practical implementation. Experimental results validate the effectiveness of the proposed approach, demonstrating notable improvements in both image quality and defect recognition accuracy over conventional techniques. The findings contribute to streamlining the inspection process, enhancing efficiency, and promoting the stable and secure operation of power systems.