不遗漏任何标签：面向所有监督模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
AI总结：
提出SuperSimpleNet，一种基于SimpleNet的高效统一表面缺陷检测模型，通过合成异常生成、增强分类头和改进学习流程，首次支持无监督、弱监督、混合监督和全监督四种模式，在四个基准上性能领先且推理时间低于10毫秒。
摘要：
表面缺陷检测是众多行业中的一项关键任务，旨在高效识别和定位制造部件上的瑕疵或不规则之处。尽管已有许多方法被提出，但许多方法未能满足工业界对高性能、高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，难以适应真实制造过程中遇到的各种数据标注情况，如无监督、弱监督、混合监督和全监督设置。为解决这些挑战，我们提出了SuperSimpleNet，一个基于SimpleNet构建的高效且适应性强的判别模型。SuperSimpleNet融合了一种新颖的合成异常生成过程、增强的分类头和改进的学习流程，使其能够在所有四种监督场景下高效训练，成为首个能够充分利用所有可用数据标注的模型。SuperSimpleNet在四个具有挑战性的基准数据集上的结果展示了其跨所有场景的性能新标准。除了准确性，它还非常快速，推理时间低于10毫秒。凭借其统一不同监督范式的能力，同时保持出色的速度和可靠性，SuperSimpleNet在应对现实制造挑战和弥合学术研究与工业应用之间差距方面迈出了有希望的一步。代码：https://github.com/blaz-r/SuperSimpleNet
英文摘要：
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
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
