無標籤不遺漏：適用於所有監督機制的統一表面缺陷檢測模型

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

August 26, 2025

作者: Blaž Rolih, Matic Fučka, Danijel Skočaj

cs.AI

摘要

表面缺陷檢測是眾多產業中的關鍵任務，旨在高效識別並定位製造部件上的瑕疵或不規則之處。儘管已提出多種方法，但許多仍無法滿足工業對高性能、效率及適應性的需求。現有方法往往受限於特定的監督情境，難以適應實際製造過程中遇到的多元數據註釋，如無監督、弱監督、混合監督及全監督設置。為應對這些挑戰，我們提出了SuperSimpleNet，這是一個基於SimpleNet的高效且適應性強的判別模型。SuperSimpleNet整合了創新的合成異常生成過程、增強的分類頭部及改進的學習程序，使其能在所有四種監督情境下進行高效訓練，成為首個能充分利用所有可用數據註釋的模型。SuperSimpleNet在四個具挑戰性的基準數據集上的表現，為所有情境設定了新的性能標準。除了精確度外，其速度極快，推理時間低於10毫秒。憑藉其統一多元監督範式的能力，同時保持卓越的速度與可靠性，SuperSimpleNet在解決現實製造挑戰及縮小學術研究與工業應用之間的差距上，邁出了充滿希望的一步。代碼：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