基于Faster RCNN的布匹瑕疵识别系统

纺织工业是我国的支柱型产业.在布匹生产过程中,布匹瑕疵是影响布匹质量的关键因素.目前纺织服装生产企业主要通过传统的人工肉眼检测布匹瑕疵,成本高、效率低,且漏检率与误检率高.本研究针对布匹数据集中类别数量不均匀的特点,对数据进行增强.在布匹瑕疵类别识别上,采用Faster RCNN模型,并针对布匹数据集中瑕疵目标小的特点,对Faster RCNN模型中的RPN网络进行改进.另外,本研究基于模型开发一款纺织布匹瑕疵识别系统,将通过模型识别出的布匹瑕疵类别结果通过可视化平台展现,同时准确标识疵点的位置.通过实验结果对比,本文的方法平均检测准确率为79.3%,相比Fast RCNN提高了5.75%.

Fabric Defect Recognition System Based on Faster RCNN

The textile industry is a pillar of China’s economy.In the supply chain of fabrics,fabric defects largely affect their quality.At present,textile production enterprises mainly rely on people’s eyes to check the quality of fabrics.This method has high cost of employment,low efficiency,and a high rate of missed and false detections,failing to meet the requirement for high-speed industrial development.This study enhances the data to address the uneven number of categories in the data set.Categories of fabric defects are identified by the Faster RCNN model,and the RPN network in the Faster RCNN model is improved regarding the small concentrated defect targets in the dataset.In addition,this study develops a fabric defect recognition system to display categories of fabric defects and precisely locate the defects.Through the comparison of experimental results,the average detection rate of this method is 79.3%,which is 5.75%higher than that of Fast RCNN.