The aim of surface defect detection is to identify and localise abnormal regions on the surfaces of captured objects, a task that's increasingly demanded across various industries. Current approaches frequently fail to fulfil the extensive demands of these industries, which encompass high performance, consistency, and fast operation, along with the capacity to leverage the entirety of the available training data. Addressing these gaps, we introduce SuperSimpleNet, an innovative discriminative model that evolved from SimpleNet. This advanced model significantly enhances its predecessor's training consistency, inference time, as well as detection performance. SuperSimpleNet operates in an unsupervised manner using only normal training images but also benefits from labelled abnormal training images when they are available. SuperSimpleNet achieves state-of-the-art results in both the supervised and the unsupervised settings, as demonstrated by experiments across four challenging benchmark datasets. Code: https://github.com/blaz-r/SuperSimpleNet .

翻译：表面缺陷检测的目标是识别并定位被捕获物体表面的异常区域，这一任务在各行各业中的需求日益增长。现有方法往往难以满足这些行业对高性能、一致性、快速运行以及充分利用全部可用训练数据能力的广泛需求。为弥补这些不足，我们提出了 SuperSimpleNet，这是一种由 SimpleNet 发展而来的创新型判别模型。该先进模型显著提升了其前代模型的训练一致性、推理时间以及检测性能。SuperSimpleNet 在仅使用正常训练图像时以无监督方式运行，但当有标记的异常训练图像可用时，也能从中受益。在四个具有挑战性的基准数据集上的实验表明，SuperSimpleNet 在监督和无监督两种设置下均取得了最先进的结果。代码：https://github.com/blaz-r/SuperSimpleNet 。