FABRIC DEFECT DETECTION METHOD BASED ON DEEP CONVOLUTIONAL NEURAL NETWORK AND VISUAL SALIENCY

摘要

A fabric defect detection method based on a deep convolutional neural network and visual saliency, wherein same falls within the technical field of image processing. The method comprises carrying out processing based on a defect region positioning module and a defect semantic segmentation module, wherein the defect region positioning module uses two deep learning models, i.e. a local convolutional neural network and a global convolutional neural network, for fusion, automatically extracts advanced features of a fabric defect and applies same to a defect image, and obtains precise positioning of a defect region; and the defect semantic segmentation module uses a positioning result of the defect region, and in conjunction with a super pixel image segmentation method based on visual saliency, acquires a defect priori foreground point and precisely segments a defect target, and finally realizes defect detection. The method has good adaptability to fabric images and a high precision, and can effectively detect a defect in the fabric image under the conditions of a complex background and noise interference.