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.

This paper is about the detection of tire defects in multi-textural radiographic images. We consider the tire defects characterization problem in ways of local regularity analysis and scale characteristic. Optimal scale and threshold parameters are selected using a defect edge measurement model to frame defect edge detection. This framework distinguishes the defects from the background textures. Finally, a novel method for detection of tire defects is proposed based on wavelet multiscale analysis. We provide examples with a consistent dataset of 400 images selected over 3700 industrial images in order to illustrate and validate the obtained results which demonstrate substantial improvement over the state of the art.

Automated tire visual inspection plays an extraordinary important role in ensuring tire quality and driving safety. Due to the anisotropic complex multi texture and defect diversity characteristic of tire radiographic image, tire intelligent visual inspection has become one of the technical bottlenecks of intelligent manufacturing. In this work, a novel tire defect detection model using Concise Semantic Segmentation Network (Concise-SSN) is investigated for automated tire visual inspection. We perform an end-to-end pixel-wise tire defect detection by combining the power of an optimized semantic segmentation network and a compact convolutional neural network for classification. It can achieve the end-to-end pixel-wise full class defect detection and classification. The experimental results show superior performance on defect segmentation and classification tasks compared to state-of-the-art models with smaller model size and faster computation. Comparative experiments indicated that our Concise-SSN achieves the mPA score of 85.13%, the mIoU score of 77.34% on our test set. The accuracy of defect classification is 96.5% on average. Finally, we show faster computation (0.132 seconds per image) with competitive results on our dataset, which can meet the needs of online tire detection.

Accurate prediction of subcellular location of apoptosis proteins combining Chou’s PseAAC and PsePSSM based on wavelet denoising Accurate prediction of subcellular location of apoptosis proteins combining Chou’s PseAAC and PsePSSM based on wavelet denoising