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 article 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 (HLU ² -Net) is proposed. In HLU ² -Net, the novel residual U-structure is used to replace encode-decode block of U-Net for fusing multiscale and multilevel 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 the 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.