Human-Machine Collaborative Classification Model for Industrial Product Defect

Yiyang Liu, Ke Wang, Xinle Cheng

Abstract: In this paper, a human-machine collaborative classification model based on Deep Forest (HMDF), is proposed for industrial product defect classification. Defect images are firstly classified by deep forest consisting of the multi-grained scanning module and the cascaded forest module. Based on the first-round result, difficult samples with low classification con-fidence are filtered out. A fraction of random selected difficult samples are manually identified and then used to classify the remaining difficult ones with the K-Nearest Neighbor (KNN) method with features extracted by the deep forest. The results of KNN are finally used to update the first-round classification results to correct errors in the initial results. The experimental results on the public data sets and the real-world data collected from the production line indicate that the proposed model could achieve superior performance compared with the baseline models in the industrial product defect classification task.