Bibliographic Details
| Title: |
Improved RT-DETR Network for High-Quality Defect Detection on Digital Printing Fabric. |
| Authors: |
Su, Zebin1,2 (AUTHOR), Shao, Yunlong1,2 (AUTHOR), Li, Pengfei1,2 (AUTHOR) li6208@163.com, Zhang, Xingyi1,2 (AUTHOR), Zhang, Huanhuan1,2 (AUTHOR) |
| Source: |
Journal of Natural Fibers. Dec2025, Vol. 22 Issue 1, p1-13. 13p. |
| Subjects: |
Digital printing, Detection algorithms, Loss functions (Statistics), Machine learning, Defect tracking (Computer software development), Acquisition of data, Surface defects |
| Abstract (English): |
Digital printing technology has been successfully implemented in actual production within factories. However, issues with various digital printing heads can still lead to defects in printed fabrics, reducing the pass rate of digital printing textiles. To promptly detect these printing defects, we have constructed a dataset of fabric defects caused by digital printing head malfunctions and propose a high-quality digital printing defect detection model tailored to our self-built dataset. Our model is built upon the Real-Time Detection Transformer (RT-DETR) framework, with an added detection head designed for detailed processing in complex backgrounds. We also incorporated an inverted residual mobile block (iRMB) to integrate attention mechanisms into the network's feature extraction process, and improved the bounding box loss function to enhance the model's detection accuracy. Experimental results demonstrate that our model achieves state-of-the-art accuracy on the COCO metrics, with a detection accuracy of 0.588 on the AP index, compared to other advanced detection models. This method effectively identifies fabric defects caused by nozzle failures in digital printing equipment, offering a novel solution for fabric defect detection. [ABSTRACT FROM AUTHOR] |
| Abstract (Chinese): |
数字印花技术已成功应用于工厂的实际生产中. 然而，各种数字打印头的问题仍然会导致打印织物出现缺陷，降低数字打印纺织品的通过率. 为了及时检测这些印刷缺陷，我们构建了一个由数字印刷头故障引起的织物缺陷数据集，并提出了一个针对我们自建数据集的高质量数字印刷缺陷检测模型. 我们的模型基于实时检测变换器（RT-DETR）框架构建，并添加了一个检测头，用于在复杂背景下进行详细处理. 我们还引入了反向残差移动块（iRMB），将注意力机制整合到网络的特征提取过程中，并改进了边界框损失函数，以提高模型的检测精度. 实验结果表明，与其他先进的检测模型相比，我们的模型在COCO指标上达到了最先进的精度，在AP指数上的检测精度为0.588。该方法有效地识别了数字印刷设备中喷嘴故障引起的织物缺陷，为织物缺陷检测提供了一种新的解决方案。 [ABSTRACT FROM AUTHOR] |
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| Database: |
Engineering Source |
