A feature-based transfer-YOLOv5 model for rapid defect inspection in large mass magnetic tile manufacturing

Abstract

Image super-resolution technology successfully overcomes the limitation of excessively large pixel size in infrared detectors and meets the increasing demand for high-resolution infrared image information. In this paper, the superresolution reconstruction of infrared images based on a convolutional neural network with a priori for high frequency information is reported. The main network structure is based on residual blocks, BN blocks that are not suitable for the super-resolution task are removed. The introduction of residual learning reduces computational complexity and accelerates network convergence. Multiple convolution layers and deconvolution layers respectively implement the extraction and restoration of the features in infrared images. images are divided into high frequency and low frequency parts. The low frequency part is the image of down-sampling, while the high frequency information is obeyed a simple case-agnostic distribution, which is equivalent to having a prior of high frequency information for the super-resolution network, Which is captures some knowledge on the lost information in the form of its distribution and embeds it into model’s parameters to mitigate the ill-posedness. Compared with the other previously proposed methods for infrared information restoration, our proposed method shows obvious advantages in the ability of high-resolution details acquisition.