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.

This study constructs a global timber trade network (2004-2023) to model underload cascading failures, assessing node-level disruptiveness and resilience. The network has grown structurally integrated, with density rising and path length shortening. Export-oriented economies (e.g., Canada, Vietnam) prove more susceptible to cascading failures than import-oriented ones (e.g., China, Japan), confirming upstream-to-downstream failure propagation. Node disruptiveness is heterogeneous: major exporters (Canada, Germany) cause the largest efficiency losses, while intermediaries (South Africa) impair connectivity. Out-strength and betweenness centrality are key determinants of disruptiveness. Resilience in 2023 varies widely; China's diversified sourcing enhances its resilience, whereas the U.S., Japan, and South Korea face higher vulnerability due to supply concentration. Overall, declining resilience among major importers underscores growing systemic risks. Although strategic diversification improves individual positions, large-scale disruptions remain a considerable threat, highlighting the imperative for optimized sourcing and robust contingency planning to stabilize global timber flows.