DWBA-YOLO: A Dual-Layer Weighted Background-aware Network for Multi-Scale Particleboard Surface Defect Detection

Abstract

Automated defect detection is crucial for particleboard manufacturing, enabling precise quality control and improved production efficiency. However, existing approaches face three key challenges: small-scale defects, low visual contrast between defects and surrounding regions, and severe texture interference from complex backgrounds, which collectively undermine feature extraction and multi-scale representation. To address these issues, this study developed DWBA-YOLO, a multi-scale surface defect detection network tailored for complex texture scenarios. First, an Adaptive Dual-layer Weighting Mechanism (ADWM) was introduced, where Intra-Feature Weighting suppresses texture-dominated channel responses while Cross-Feature Weighting adaptively calibrates contributions from different pyramid levels. Second, an Adaptive Spatial Feature Fusion head was designed to learn spatially varying fusion weights and to mitigate cross-scale inconsistencies while maintaining lightweight overhead. Third, Normalized Wasserstein Distance was incorporated to enhance small-scale defect localization. Extensive experiments demonstrated the effectiveness of the method. On a proprietary particleboard defect dataset, DWBA-YOLO improved recall by 4.7%, precision by 4.2%, mAP@50 by 3%, and mAP@50:95 by 2.5% compared with YOLOv8n, while reducing computational complexity by 43%. These results indicate that DWBA-YOLO is effective and practical for real-time particleboard defect detection.