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Defect detection and classification technology has changed from traditionalartificial visual inspection to current intelligent automated inspection, butmost of the current defect detection methods are training related detectionmodels based on a data-driven approach, taking into account the difficulty ofcollecting some sample data in the industrial field. We apply zero-shotlearning technology to the industrial field. Aiming at the problem of theexisting "Latent Feature Guide Attribute Attention" (LFGAA) zero-shot imageclassification network, the output latent attributes and artificially definedattributes are different in the semantic space, which leads to the problem ofmodel performance degradation, proposed an LGFAA network based on semanticfeedback, and improved model performance by constructing semantic embeddedmodules and feedback mechanisms. At the same time, for the common domain shiftproblem in zero-shot learning, based on the idea of co-training algorithm usingthe difference information between different views of data to learn from eachother, we propose an Ensemble Co-training algorithm, which adaptively reducesthe prediction error in image tag embedding from multiple angles. Variousexperiments conducted on the zero-shot dataset and the cylinder liner datasetin the industrial field provide competitive results.

Zero-sample surface defect detection and classification based on semantic feedback neural network