HLU2-Net: A Residual U-Structure Embedded U-Net With Hybrid Loss for Tire Defect Inspection

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Abstract

Intelligent defect detection have been widely studied and applied in many industrial fields. However, intelligent tire defect inspection remains a challenging task due to tire radiographic images’ anisotropic multi-texture background in which a variety of defects may appear with intra class dissimilarity and inter class similarity. This paper addresses the problem intelligent tire defect detection using end-to-end saliency detection network. A novel end-to-end residual U-structure embedded U-Net with hybrid loss function and coordinate attention module (HLU2-Net) is proposed. In HLU2-Net, the novel residual U-structure is used to replace encode-decode block of U-Net for fusing multi-scale and multi-level features, and a hybrid loss is presented to guide defect detection for complete and clean defect mask. Moreover, a coordinate attention module is introduced to highlight useful features and weaken irrelevant features. Comparative experimental results verify that our method outperforms state-of-the-art methods on our dataset according to six evaluation metrics. Additionally, we demonstrate that the computing efficiency of our method can meet online visual detection on tire production line.

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