| 王峰.基于超分辨率模型与YOLO V4的织物疵点检测[J].轻工机械,2022,40(5):60-66 |
| 基于超分辨率模型与YOLO V4的织物疵点检测 |
| Fabric Defect Detection Based on Super Resolution and YOLO V4 |
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| DOI：10.3969/j.issn.1005 2895.2022.05.009 |
| 中文关键词: 织物疵点 超分辨率重构 改进SRGAN算法 数据扩充 YOLO V4网络 |
| 英文关键词:fabric defects super resolution reconstruction improved SRGAN（Super Resolution Generative Adversarial Network） |
| 基金项目:西安市现代智能纺织装备重点实验室基金项目（2019220614SYS021CG043）。 |
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| 中文摘要: |
| 针对工业条件限制下采集的印花布数据集图像分辨率低、检测效果差等问题，课题组提出基于超分辨率模型SRGAN与YOLO V4网络的织物疵点检测方法，并对SRGAN算法进行改进。课题组首先使用改进的SRGAN算法对原数据集进行超分辨率重构，提高图像分辨率；然后将重构图翻转变化与原图共同作为数据集输入YOLO V4进行网络训练；最后通过YOLO V4网络检测印花布表面疵点。实验结果表明：该方法可提高低分辨率织物图疵点检测效果，准确率高达90.29%，比超分辨率重构前提升了13.19%，能实现实时定位疵点的准确位置并输出疵点类别。 |
| 英文摘要: |
| Aiming at the problems of low image resolution and poor detection effect of the collected printed fabric data set under industrial conditions, a fabric defect detection method based on super resolution model (SRGAN) and YOLO V4 networks was proposed, and the SRGAN algorithm was improved. Firstly, the improved SRGAN algorithm was used to reconstruct the original data set with super resolution to improve the image resolution; secondly, the inverted changes of the reconstructed image and the original image were input into the YOLO V4 network as the dataset for training; finally, the surface defects of printed fabric were detected by YOLO V4 network. The experimental results show that the proposed method can improve the effect of low resolution fabric image defect detection, and the accuracy rate is as high as 90.29%, which is 13.19% higher than that before super resolution reconstruction. Moreover, it can realize the real time positioning of defects and output the defect category. |
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