Tire Defect Detection by Dual-Domain Adaptation-Based Transfer Learning Strategy

Abstract

In tire defect detection, an X-ray image sensor is utilized to collect samples, upon which automatic defect detection methods are constructed. The performance of existing defect detection methods is reduced significantly when working condition shifts. Fortunately, domain adaptation can address this domain shift problem effectively. However, previous methods only align the marginal distribution or conditional distribution, while ignoring the unified adaptation of the two distributions, and may result in an unsatisfactory transfer effect. To this end, this article proposes a dual-domain adaptation model, which harnesses the adaptation of global marginal distribution and fine-grained conditional distribution simultaneously. Specifically, to learn the feature with the consideration of intraclass compact and interclass discriminative, the relative importance of marginal distribution and conditional distribution is measured by a dynamic adaptive factor. In addition, a multihead self-attention (MSA) mechanism is designed in the feature extractor for global context modeling. To evaluate the effectiveness of the proposed method, the tire defect detection task on five kinds of familiar defects, namely slack, bend, overlap, impurity, and bubble, was constructed. Compared with other existing domain adaptation models, the proposed model can achieve 96.39% detect detection accuracy. The experimental results demonstrate the effectiveness of the proposed method in tire defect detection under the domain shift.