HLU<sup>2</sup>-Net: A Residual U-Structure Embedded U-Net With Hybrid Loss for Tire Defect Inspection
Zhouzhou Zheng, Huanbo Yang, Liang Yong Zhou, Bin Yu, Yan Zhang
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.