High-Precision Fabric Defect Detection via Adaptive Shape Convolutions and Large Kernel Spatial Modeling

2025-01-24

AI Summary

To address the low detection accuracy and slow inference speed for minute, morphologically diverse fabric defects in textile manufacturing, this paper proposes Fab-ASLKS, a novel YOLOv8s-based detection framework. It introduces two key modules: the Adaptive Shape Convolution Module (ASCM), which enhances morphological adaptability in neck-level feature fusion, and the Large-Kernel Shifted Convolution Module (LKSCM), which strengthens spatial modeling capacity in the backbone—both overcoming inherent limitations of the standard C2f structure. Through synergistic optimization of the neck and backbone, the framework significantly improves multi-scale defect representation with negligible computational overhead. Evaluated on the Tianchi Fabric Defect Dataset, Fab-ASLKS achieves a 5.0% absolute gain in mAP@50, particularly enhancing detection sensitivity for subtle defects under strong texture interference, while maintaining real-time inference capability.

Technology Category

• Computer Vision: Adversarial Attacks & Robustness
• Machine Learning: Adversarial Learning & Robustness
• Search and Optimization: Learning to Search

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
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications

Abstract

Detecting fabric defects in the textile industry remains a challenging task due to the diverse and complex nature of defect patterns. Traditional methods often suffer from slow inference speeds, limited accuracy, and inadequate recognition rates, particularly in scenarios involving intricate or subtle defects. To overcome these limitations, we introduce Fab-ASLKS, an advanced fabric defect detection framework built upon the YOLOv8s architecture. Fab-ASLKS incorporates two key modules: (1) the Adaptive Shape Convolution Module (ASCM), which leverages adaptive shape convolution within the Neck to enhance feature fusion and improve efficiency by extending the capabilities of the standard C2f structure, and (2) the Large Kernel Shift Convolution Module (LKSCM), designed to emulate large kernel effects within the Backbone, enabling superior spatial information extraction. These modules collaboratively optimize feature extraction and information integration across the network. Extensive experiments conducted on the Tianchi fabric defect detection dataset demonstrate that Fab-ASLKS achieves a 5% improvement in mAP@50 over the baseline, showcasing its capability to deliver high precision and efficiency.

Problem

• Textile Defect Detection
• Efficiency
• Accuracy

Innovation

• Adaptive Shape Convolution Module (ASCM)
• Large Kernel Shift Convolution Module (LKSCM)
• YOLOv8s-based Fabric Defect Detection