Biological visual attention convolutional neural network for aircraft skin defect detection

Abstract

Inspired by the biological visual attention mechanism, we propose a visual attention convolutional neural network to solve the problem of identifying aircraft skin, but showing that the aircraft s kin image collected by the camera is rarely illuminated, and the contrast between defects and background is low and difficult to identify. Firstly, the U-Net with the encoder-decoder structure is used to initially segment the skin image that are provided by Airlines. Then, the residual block is introduced into the U-Net to enhance the propagation ability of the features and extract more defect detail feature information. Finally, the visual attention mechanism is used to increase the weight of the defect area to reduce the influence of uneven illumination on the model. The experimental results show that the proposed model has better defect segmentation effects in both visual effects and objective evaluation indexes. Our algorithm not only enhances the accuracy and effectiveness of skin defect identification but also provides management personnel with a more relevant tool, promoting the intelligence and information of air maintenance. This contributes to excellent management support for aviation companies and related industries.