基于元学习的带钢表面缺陷小样本语义分割

Few-Shot Semantic Segmentation of Strip Steel Surface Defects Based on Meta-Learning

由于缺少带钢表面缺陷样本,使得深度神经网络在带钢表面缺陷检测的应用受到了限制,为解决这一实际问题,提出了一种基于元学习思想的小样本语义分割深度学习方法.该方法引入了多尺度解码器和注意力机制.多尺度解码器能够聚合不同尺度的缺陷特征信息,提高网络的分割精度.注意力机制能够有效增强缺陷信息表达,并且抑制背景信息的干扰.此外,构建了一个带钢表面缺陷语义分割数据集,该数据集包含9类带钢表面缺陷.在该数据集上进行了相关实验,结果表明本文方法在平均交并比和前景-背景交并比指标上优于PFENet,SCLNet和HSNet等方法.

Due to the limited availability of strip surface defect samples,the application of deep neural networks in strip surface detection is constrained.To solve this practical issue,a metalearning‐based few‐shot semantic segmentation method is proposed.A multi‐scale decoder and attention mechanism are used in the proposed method.The multi‐scale decoder can aggregate the defect information at different scales and improve segmentation accuracy of the proposed network.The attention mechanism can effectively improve the expression of defect features and suppress the interference of defect background information.In addition,a novel few‐shot steel strip surface defect semantic segmentation dataset is constructed including nine classes of strip steel surface defects.Comparison experiments on the proposed dataset show that the proposed method is superior to similar few‐shot segmentation methods such as PFENet,SCLNet,and HSNet in terms of evaluation index mean intersection over union and foreground‐background intersection over union.