Wheel Hub Defects Image Recognition Base on Zero-Shot Learning

Abstract

In the wheel hub industry, the quality control of the product surface determines the subsequent processing, which can be realized through the hub defect image recognition based on deep learning. Although the existing methods based on deep learning have reached the level of human beings, they rely on large-scale training sets, however, these models are completely unable to cope with the situation without samples. Therefore, in this paper, a generalized zero-shot learning framework for hub defect image recognition was built. First, a reverse mapping strategy was adopted to reduce the hubness problem, then a domain adaptation measure was employed to alleviate the projection domain shift problem, and finally, a scaling calibration strategy was used to avoid the recognition preference of seen defects. The proposed model was validated using two data sets, VOC2007 and the self-built hub defect data set, and the results showed that the method performed better than the current popular methods.

Generative and Contrastive Combined Support Sample Synthesis Model for Few-/Zero-Shot Surface Defect Recognition

“…In order to detect novel defect types (classes with no support set) that arise unexpectedly in real production environments, a few studies have attempted to apply Zero-Shot learning to defect recognition [16]- [18]. The basic idea of zero-shot learning for visual computing is to train a cross-modal network to synthesize the visual features from the corresponding semantic features [19]- [22].…”