Three-branch convolutional network fabric defect detection method based on weak supervised learning

Abstract

The invention provides a three-branch convolutional network fabric defect detection method based on weak supervised learning, and the method comprises the steps: firstly, building a multi-example learning detection network based on a mutual exclusion principle in a weak supervised network, so as to carry out the training through an image-level label; then, establishing a three-branch network framework, and adopting a long connection structure so as to extract and fuse the multi-level convolution feature map; utilizing the SE module and the cavity convolution to learn the correlation between channels and expand the convolution receptive field; and finally, calculating the positioning information of the target by using a class activation mapping method to obtain the attention mapping of thedefect image. According to the method, the problems of rich textural features and defect label missing contained in the fabric picture are comprehensively considered, and by adopting a weak supervision network mechanism and a mutual exclusion principle, the representation capability of the fabric picture is improved while the dependence on the label is reduced, so that the detection result has higher detection precision and adaptivity.

Description

technical field [0001] The invention relates to the technical field of textile image processing, in particular to a three-branch convolutional network fabric defect detection method based on weakly supervised learning. Background technique [0002] Fabric defect detection plays a vital role in the quality control of fabric products and has been the main research direction of scientific researchers. However, due to the wide variety of fabrics, a manufacturer can produce hundreds of fabrics with different textures at the same time, which leads to extremely complicated defect detection. Traditional fabric inspection is mainly carried out by artificial vision. Due to the sensory problems of the workers themselves and the fatigue of long-term continuous work, missed inspections and false inspections often occur. This results in higher labor costs and lower productivity. Therefore, machine vision that detects defects quickly and automatically provides an ideal solution for fabr...

AI Technical Summary

Problems solved by technology

[0007] Aiming at the problem that the existing deep learning fabric detection method has been limited by the lack of labels in the data set, the present invention proposes a three-branch convolutional network fabric defect detection method based on weakly supervised learning, using mutual exclusion in the weakly supervised network Establish a multi-instance learning detection network in principle; use multi-level feature fusion and the expansion of receptive field to improve the representation ability of fabric images; use hole convolution group and Squeeze-and-Excitation (SE) module to enhance the robustness of the network In order to better predict the defect area and improve the detection accuracy of the defect; finally, using the localization method (class activation map, CAM) in the weak supervision network to calculate the localization information of the target, the texture information can be used more effectively. Locate object regions for better training cues

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