No Label Left Behind: A Unified Surface Defect Detection Model for all Supervision Regimes
Blaž Rolih, Matic Fučka, Danijel Skočaj
2025-09-02
Summary
This paper introduces SuperSimpleNet, a new computer vision model designed to quickly and accurately find flaws on products coming off a manufacturing line.
What's the problem?
Currently, finding defects in manufactured products is tough because existing methods aren't flexible enough. They often need a lot of specifically labeled data, and real-world factories have data labeled in different ways – sometimes with detailed labels, sometimes with very few, and sometimes with no labels at all. It's hard to have one system that works well with all these different types of data, and many systems aren't fast enough for use in a fast-paced factory.
What's the solution?
The researchers created SuperSimpleNet, which builds upon a simpler existing model. They improved it by creating a way to automatically generate examples of what defects might look like, making the model better at spotting anomalies. They also refined how the model makes its final decision about whether something is a defect or not, and improved the training process so it can learn effectively no matter how much labeled data is available. This allows it to work well whether you have a lot of detailed labels, a few rough labels, or no labels at all.
Why it matters?
SuperSimpleNet is important because it's the first model that can handle all different kinds of data labeling situations in manufacturing. It’s also very fast – it can identify defects in less than 10 milliseconds, which is crucial for keeping production lines moving. This means it has the potential to be used in real factories to improve quality control and reduce waste, bridging the gap between research and practical application.
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: https://github.com/blaz-r/SuperSimpleNet