基于改进Faster R-CNN的织物疵点检测算法Research on improved Faster R-CNN algorithm for fabric defect detection
孙旋;高小淋;曹高帅;
摘要(Abstract):
针对织物疵点面积小且长宽比跨度大的问题,提出一种基于改进Faster R-CNN的多种织物疵点检测算法。以Faster R-CNN检测算法为基础,选取优化后的ResNet50作为Faster R-CNN的主干网络,在保持ResNet50深度不变的情况下,拓宽残差结构宽度,通过调整网络部分层结构并优化网络参数,使网络提取更多特征信息并减少网络计算量。针对织物疵点检测精度低的问题,在Faster R-CNN中引用FPN网络进行多尺度预测,并将改进的K-means聚类算法生成的预测框代替原Faster R-CNN中人工设计的预测框,增强网络聚焦“小目标”疵点的特征能力,进一步提高疵点检测精度。实验结果表明:相较于原Faster R-CNN,基于改进的Faster R-CNN在平均精度上提高了6.6%,且对于“小目标”与“细长型”疵点,识别率分别高达95%与97%,在织物疵点检测中具有较好的应用价值。
关键词(KeyWords): 织物疵点;疵点检测;Faster R-CNN;优化ResNet50;改进的K-means
基金项目(Foundation): 国家自然科学基金项目(52065016)
作者(Authors): 孙旋;高小淋;曹高帅;
DOI: 10.19333/j.mfkj.20220305708
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