Network Knowledge Prior Guided Learning for Data-Efficient Surface Defect Detection

2026-05-17

AI Summary

This work addresses the performance bottlenecks and limited trustworthiness of deep learning models in industrial surface defect detection, which stem from their strong data dependency and poor interpretability. To this end, the authors propose a knowledge-guided loss function that integrates interpretability into the training process without incurring additional inference overhead. The approach employs a two-stage training strategy: first, a primary classification network is trained and used to generate saliency maps as prior knowledge; then, a multi-task learning framework is constructed, where an auxiliary task enforces consistency between the saliency maps of the final model and those of the primary model. Experiments on multiple public defect datasets demonstrate that the proposed method significantly improves baseline models in terms of accuracy and average precision (AP), while producing more focused and human-aligned saliency maps, thereby achieving a synergistic optimization of performance and interpretability.

Abstract

Deep learning-based methods have become the de facto standard for industrial defect detection. However, their data-hungry nature and inherent "black-box" characteristics often lead to performance bottlenecks and limited trustworthiness in real-world applications. To address these challenges, this paper proposes a novel knowledge-guided loss function that seamlessly integrates model interpretability into the training process without incurring any additional inference cost. Our method operates in two phases: first, a primary classification network is trained, and its explanations, in the form of saliency maps, are generated as prior knowledge. Second, a multi-task learning framework is established, where the main task performs classification, and an auxiliary task imposes consistency between the saliency maps of the final model and the primary model. This consistency is enforced by a dedicated knowledge-guided loss term, effectively acting as a powerful regularizer to steer the model towards robust feature representations. Extensive experiments on multiple public defect datasets demonstrate that our approach consistently enhances the performance of baseline models in terms of accuracy and AP. Moreover, visual analysis reveals that the proposed method yields more concentrated and human-intelligible saliency maps. This work presents a simple yet effective paradigm for bridging the gap between model performance and interpretability, paving the way for more reliable and high-performing vision systems in industrial quality inspection.

Problem

• data efficiency
• model interpretability
• surface defect detection
• black-box models
• industrial inspection

Innovation

• knowledge-guided learning
• saliency map consistency
• interpretable deep learning
• data-efficient defect detection
• multi-task regularization