用自适应卷积和大核建模提升布料缺陷检测精度
High-Precision Fabric Defect Detection via Adaptive Shape Convolutions and Large Kernel Spatial Modeling
- 引入自适应形状卷积与大核位移卷积模块，优化特征融合与空间建模
- 在天池数据集上比基线模型mAP@50提升5%，达高精度检测
- 适合工业质检场景，尤其擅长识别复杂细微缺陷
纺织品缺陷检测因缺陷形态多样且复杂，仍具挑战性。传统方法普遍存在推理速度慢、精度低、识别率不足等问题，尤其在处理复杂或细微缺陷时表现不佳。为此，本文提出基于YOLOv8s架构的Fab-ASLKS框架，引入两个核心模块：(1) 自适应形状卷积模块（ASCM），在Neck中通过自适应形状卷积增强特征融合效率，扩展标准C2f结构能力；(2) 大核位移卷积模块（LKSCM），在Backbone中模拟大核效果，实现更优的空间信息提取。两个模块协同优化网络中的特征提取与信息整合。在天池布料缺陷检测数据集上的大量实验表明，Fab-ASLKS相较基线模型在mAP@50上提升5%，展现出高精度与高效性。
原文摘要 · Abstract (English)
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.
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