计算机科学 ›› 2023, Vol. 50 ›› Issue (11): 151-159.doi: 10.11896/jsjkx.221100023
王尚尚, 金城
WANG Shangshang, JIN Cheng
摘要: 基于图像重构的方法是表面异常检测中一类广泛使用的方法。该类方法仅期望模型较好地重构正常模式,并通过异常区域较大的重构误差来检测和定位异常。已有方法一方面易出现“泛化”过好的现象,异常区域也被高保真地重构了出来;另一方面仅在图像空间度量重构误差,并没有真正捕捉到原图和重构图之间的语义差异。为了解决上述问题,文中提出了由重构网络和识别网络组成的表面异常检测框架,其中重构网络嵌入了多尺度位置增强动态原型单元,强化了对正常模式的学习;识别网络进行了输入图和重构图的多尺度深度特征融合,从多个尺度利用了重构前后的语义差异信息,强化了对重构差异的识别。在MVTec数据集上,所提方法在异常检测任务上取得了99.5%的 AUROC,在异常定位任务上取得了98.5% 的AUROC,以及95.0%的RPO检测表现,与之前基于重构的表面异常检测方法相比取得了较大提升。
中图分类号:
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