WD-SEG: A Deep Learning Framework for Delicate and Accurate Wood Defect Segmentation

Abstract

Precise segmentation of subtle wood defects is crucial for optimizing wood utilization and product value. Despite the prevalence of deep learning in wood defect detection, its deployment in real-world forestry environments is impeded by three primary challenges: 1: The limited capacity of traditional models to represent low-contrast, faint defect features; 2: feature ambiguity caused by complex background interference; and 3: entrapment in local optima because of insufficient global feature integration. To surmount these obstacles, this study proposes WD-SEG (Wood Defect Segmentation), a high-performance model tailored for complex forestry scenarios. The architecture integrates three core modules: an Augmented Feature Network (AFN) to mitigate spatial information loss; a Threshold Filtering Network (TFN), which leverages cosine similarity to adaptively suppress background noise; and a novel Interstellar Collision Optimization (ICO) algorithm to accelerate convergence and bypass local optima. Experimental evaluations on the wood defect training dataset demonstrate that WD-SEG outperforms state-of-the-art models, achieving an Intersection over Union (IoU) of 87.97% and an accuracy of 90.02%. Furthermore, generalization tests on wood defect datasets confirm the model’s robustness, yielding an IoU of 86.50%. By introducing a novel “Enhance-Filter-Accelerate” framework, this study provides a precise, robust solution for automated wood quality inspection in resource-constrained environments.