无标签遗漏：面向全监督范式的统一表面缺陷检测模型

摘要

表面缺陷检测是众多行业中的一项关键任务，旨在高效识别并定位制造部件上的瑕疵或异常。尽管已提出多种方法，但许多仍难以满足工业对高性能、效率和适应性的需求。现有方法往往局限于特定的监督场景，难以适应实际制造过程中遇到的各种数据标注形式，如无监督、弱监督、混合监督和全监督设置。为应对这些挑战，我们提出了SuperSimpleNet，这是一个基于SimpleNet构建的高效且适应性强的判别模型。SuperSimpleNet引入了新颖的合成异常生成过程、增强的分类头以及改进的学习流程，使其能够在所有四种监督场景下进行高效训练，成为首个能够充分利用所有可用数据标注的模型。通过在四个具有挑战性的基准数据集上的表现，SuperSimpleNet为所有场景设立了新的性能标准。除了高精度外，它还非常快速，推理时间低于10毫秒。凭借其统一多样化监督范式的能力，同时保持卓越的速度和可靠性，SuperSimpleNet在解决现实制造挑战、弥合学术研究与工业应用之间的差距方面迈出了有希望的一步。代码：https://github.com/blaz-r/SuperSimpleNet

English

Surface defect detection is a critical task across numerous industries, aimed at efficiently identifying and localising imperfections or irregularities on manufactured components. While numerous methods have been proposed, many fail to meet industrial demands for high performance, efficiency, and adaptability. Existing approaches are often constrained to specific supervision scenarios and struggle to adapt to the diverse data annotations encountered in real-world manufacturing processes, such as unsupervised, weakly supervised, mixed supervision, and fully supervised settings. To address these challenges, we propose SuperSimpleNet, a highly efficient and adaptable discriminative model built on the foundation of SimpleNet. SuperSimpleNet incorporates a novel synthetic anomaly generation process, an enhanced classification head, and an improved learning procedure, enabling efficient training in all four supervision scenarios, making it the first model capable of fully leveraging all available data annotations. SuperSimpleNet sets a new standard for performance across all scenarios, as demonstrated by its results on four challenging benchmark datasets. Beyond accuracy, it is very fast, achieving an inference time below 10 ms. With its ability to unify diverse supervision paradigms while maintaining outstanding speed and reliability, SuperSimpleNet represents a promising step forward in addressing real-world manufacturing challenges and bridging the gap between academic research and industrial applications. Code: https://github.com/blaz-r/SuperSimpleNet

无标签遗漏：面向全监督范式的统一表面缺陷检测模型

No Label Left Behind: A Unified Surface Defect Detection Model for all Supervision Regimes

摘要

Support