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

TL;DR

SuperSimpleNet is a versatile surface defect detection model that efficiently handles various supervision types, achieving high accuracy and speed, thus bridging the gap between research and industrial needs.

Contribution

It introduces SuperSimpleNet, a unified model capable of leveraging all supervision regimes for surface defect detection, with novel synthetic anomaly generation and enhanced learning procedures.

Findings

Sets new performance standards on four benchmark datasets.

Achieves inference times below 10 milliseconds.

Effectively handles unsupervised, weakly supervised, mixed, and fully supervised scenarios.

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