Self-Adaptive Gamma Context-Aware SSM-based Model for Metal Defect Detection

2025-03-03

AI Summary

To address the insufficient robustness in industrial metal surface defect detection caused by non-uniform illumination, large grayscale variations, and multi-scale defects, this paper proposes a novel method integrating adaptive Gamma correction with a context-aware state space model (SSM). We innovatively design a dynamic Gamma correction module to enhance feature representation in low-contrast regions and develop an SSM-driven multi-scale feature modeling framework. Additionally, we introduce CD5-DET, a new benchmark dataset tailored for container maintenance in port environments. Extensive experiments demonstrate substantial improvements: mAP@0.5 increases by 27.6%, 6.6%, and 2.6% on CD5-DET, NEU-DET, and GC10-DET, respectively. The proposed method significantly advances detection performance for small objects and low-contrast defects, validating its effectiveness in challenging industrial scenarios.

Technology Category

• Computer Vision: Adversarial Attacks & Robustness
• Intelligent Robots: State Estimation
• Machine Learning: Calibration & Uncertainty Quantification

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Web Mining and Content Analysis: Robustness and generalizability of Web mining methods

Abstract

Metal defect detection is critical in industrial quality assurance, yet existing methods struggle with grayscale variations and complex defect states, limiting its robustness. To address these challenges, this paper proposes a Self-Adaptive Gamma Context-Aware SSM-based model(GCM-DET). This advanced detection framework integrating a Dynamic Gamma Correction (GC) module to enhance grayscale representation and optimize feature extraction for precise defect reconstruction. A State-Space Search Management (SSM) architecture captures robust multi-scale features, effectively handling defects of varying shapes and scales. Focal Loss is employed to mitigate class imbalance and refine detection accuracy. Additionally, the CD5-DET dataset is introduced, specifically designed for port container maintenance, featuring significant grayscale variations and intricate defect patterns. Experimental results demonstrate that the proposed model achieves substantial improvements, with mAP@0.5 gains of 27.6%, 6.6%, and 2.6% on the CD5-DET, NEU-DET, and GC10-DET datasets.

Problem

• Addresses grayscale variations in metal defect detection
• Handles complex defect states with multi-scale feature extraction
• Mitigates class imbalance to improve detection accuracy

Innovation

• Dynamic Gamma Correction enhances grayscale representation.
• State-Space Search Management captures multi-scale features.
• Focal Loss mitigates class imbalance for accuracy.