Abstract
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: this https URL
Abstract (translated)
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组件上的瑕疵或不规则现象。尽管已经提出了许多方法，但很多方法未能满足工业对高性能、高效率以及灵活性的需求。现有方法通常局限于特定的监督场景，并且难以适应真实世界制造业中遇到的各种数据标注情况，如无监督、弱监督、混合监督及全监督设置等。为了解决这些挑战，我们提出了一种基于SimpleNet基础构建的高度高效和灵活的判别模型——SuperSimpleNet。SuperSimpleNet整合了新颖的人工异常生成过程、增强型分类头以及改进的学习程序，能够适应所有四种监督场景下的有效训练，使其成为首个能全面利用所有可用数据标注的模型。在四个具有挑战性的基准数据集上，SuperSimpleNet展示了其卓越性能，并证明了它可以在所有情景下设定新的标准。除了准确性之外，它还非常快速，在推理时间方面低于10毫秒。凭借统一各种监督范式的能力并同时保持出色的高速度和可靠性，SuperSimpleNet代表了一种应对现实世界制造挑战以及弥合学术研究与工业应用之间差距的有希望的进步。 代码地址：[此链接](https://this%20URL)
URL
https://arxiv.org/abs/2508.19060
