Geometric feature extraction in nanofiber membrane image based on convolution neural network for surface roughness prediction.

As a technique in artificial intelligence, a convolution neural network model has been utilized to extract average surface roughness from the geometric characteristics of a membrane image featuring micro- and nanostructures. For surface roughness measurement, e.g. atomic force microscopy and optical profiler, the previous methods have been performed to analyze a porous membrane surface on an interest of region with a few micrometers of the restricted area according to the depth resolution. However, an image from the scanning electron microscope, combined with the feature extraction process, provides clarity on surface roughness for multiple areas with various depth resolutions. Through image preprocessing, the geometric pattern is elucidated by amplifying the disparity in pixel intensity values between the bright and dark regions of the image. The geometric pattern of the binary image and magnitude spectrum confirmed the classification of the surface roughness of images in a categorical scatter plot. A group of cropped images from an original image is used to predict the logarithmic average surface roughness values. The model predicted 4.80 % MAPE for the test dataset. The method of extracting geometric patterns through a feature map-based CNN, combined with a statistical approach, suggests an indirect surface measurement. The process is achieved through a bundle of predicted output data, which helps reduce the randomness error of the structural characteristics. A novel feature extraction approach of CNN with statistical analysis is a valuable method for revealing hidden physical characteristics in surface geometries from irregular pixel patterns in an array of images.