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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 .
SuperSimpleNet：统一无监督和监督学习，欧博实现快速可靠的表面缺陷检测 表面缺陷检测的目的是识别和定位所捕获物体表面的异常区域，这是各个行业对这项任务的需求越来越大。当前的方法经常无法满足这些行业的广泛需求，包括高性能、一致性和快速操作，欧博娱乐以及利用全部可用训练数据的能力。为了解决这些差距，我们引入了 SuperSimpleNet，这是一种从 SimpleNet 发展而来的创新判别模型。这种先进的模型显着增强了其前身的训练一致性、推理时间以及检测性能。 SuperSimpleNet 仅使用正常训练图像以无监督方式运行，但也可以从标记的异常训练图像（当它们可用时）中受益。 SuperSimpleNet 在监督和无监督设置中均取得了最先进的结果，如四个具有挑战性的基准数据集的实验所证明的那样。代码：https://github.com/blaz-r/SuperSimpleNet。 (责任编辑：) |
