SPFFNet: Strip Perception and Feature Fusion Spatial Pyramid Pooling for Fabric Defect Detection

TL;DR

This paper introduces SPFFNet, a fabric defect detection model that combines a strip perception module, an enhanced spatial pyramid pooling, and a novel IoU metric, achieving significant accuracy improvements over existing methods.

Contribution

The paper presents a novel fabric defect detection model with a strip perception module, an improved spatial pyramid pooling, and a new IoU metric, enhancing detection accuracy in complex backgrounds.

Findings

Achieved up to 8.1% improvement in mAP on Tianchi dataset.

Outperformed state-of-the-art methods in fabric defect detection.

Demonstrated robustness in detecting complex shape defects.

Abstract

Defect detection in fabrics is critical for quality control, yet existing methods often struggle with complex backgrounds and shape-specific defects. In this paper, we propose an improved fabric defect detection model based on YOLOv11. To enhance the detection of strip defects, we introduce a Strip Perception Module (SPM) that improves feature capture through multi-scale convolution. We further enhance the spatial pyramid pooling fast (SPPF) by integrating a squeeze-and-excitation mechanism, resulting in the SE-SPPF module, which better integrates spatial and channel information for more effective defect feature extraction. Additionally, we propose a novel focal enhanced complete intersection over union (FECIoU) metric with adaptive weights, addressing scale differences and class imbalance by adjusting the weights of hard-to-detect instances through focal loss. Experimental results demonstrate that our model achieves a 0.8-8.1% improvement in mean average precision (mAP) on the Tianchi dataset and a 1.6-13.2% improvement on our custom dataset, outperforming other state-of-the-art methods.