Computer vision-based algorithm for precise defect detection and classification in photovoltaic modules.

ABSTRACT: In recent years, driven by advancements in the photovoltaic industry, solar power generation has emerged as a crucial energy source in China and the globe. A progressive annotation approach is employed to pinpoint and label defect samples to enhance the precision of automated detection technology for minor defects within photovoltaic modules. Subsequently, computer vision techniques are harnessed to segment photovoltaic modules and defect samples amidst intricate backgrounds accurately. Finally, a transfer learning training model is deployed to classify and identify defects effectively. The results indicate that the mask-region convolutional neural network model achieves remarkable accuracy and recall rates of 98.7% and 0.913, respectively. Furthermore, the detection speed and inference ti

SUBMITTER: Guo J 

PROVIDER: S-EPMC11623212 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature