Wood-YOLOv11: An Optimized YOLOv11-Based Model for Real-Time Pith Detection in Sawn Timber

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Abstract

The precise localization of the pith within sawn timber cross-sections is essential for improving downstream processing accuracy in modern wood manufacturing. Existing industrial workflows still rely heavily on manual interpretation, which is labor-intensive, error-prone, and unsuitable for real-time quality control. However, automatic pith detection is challenging due to the small size of the pith, its visual similarity to knots and cracks, and the dominance of negative samples (boards without visible pith) in practical scenarios. To address these challenges, this study develops Wood-YOLOv11, a task-adapted YOLOv11-based pith detection model optimized for real-time and high-precision operation in wood processing environments. The proposed approach incorporates: (1) a dedicated sawn-timber cross-section dataset including multiple species, mixed imaging sources, and clearly annotated pith positions; (2) a negative-sample-aware training strategy that explicitly leverages pithless boards and weighted binary cross-entropy to mitigate extreme class imbalance; (3) a high-resolution (840 × 840) input configuration and optimized loss weighting to improve small-target localization; and (4) a comprehensive evaluation protocol including false-positive analysis on pithless boards and comparison with mainstream detectors. Validated on a comprehensive, custom-annotated sawn timber dataset, our model demonstrates excellent performance. It achieves a mean Average Precision (mAP@0.5) of 92.1%, a Precision of 95.18%, and a Recall of 87.72%, proving its ability to handle high-texture backgrounds and small target sizes. The proposed Wood-YOLOv11 model provides a robust, real-time, and efficient technical solution for the intelligent transformation of the wood processing industry.

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