基于SSD的Fabric缺陷检测

Proceedings of the 2nd International Conference on Graphics and Signal Processing Pub Date : 2018-10-06 DOI:10.1145/3282286.3282300

Zhoufeng Liu, Shanliang Liu, Chunlei Li, S. Ding, Yan Dong

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引用次数: 26

摘要

由于织物纹理的复杂性，织物缺陷检测是一项具有挑战性的任务。深度学习技术提供了一个很有前途的解决方案。作为一种深度学习的目标检测模型。单镜头多盒检测器(Single Shot Multibox Detector, SSD)具有良好的检测性能。但原有型号的SSD可能无法检测到小对象。本文提出了一种用于织物缺陷检测的SSD模型。实验结果表明，改进的SSD模型能够准确地检测出缺陷区域。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Fabric Defects Detection based on SSD

In this paper, Fabric defect detection is a challenging task because of the complex texture. Deep learning technology provide a promising solution. As a kind of deep learning object detection model. Single Shot Multibox Detector(SSD)achieves good detection performance. However, the original SSD model may fail to detect the small objects. In this paper, we proposed a novel SSD model for fabric defect detection. Experimental results showed that the improved SSD model can accurately detect the defect region.

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Proceedings of the 2nd International Conference on Graphics and Signal Processing

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