Laboratory Validation of a Low-Cost Embedded Computer Vision System for Automated Defect Detection in Beech Sawn Timber

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Abstract

The digital transformation of the wood-processing industry increasingly relies on computer vision, artificial intelligence (AI), and embedded sensing technologies to automate timber quality assessment. This study presents the laboratory validation (TRL4) of a low-cost embedded computer-vision demonstrator for automated surface-defect detection in beech (Fagus sylvatica) sawn timber, developed as the first operational module of the SMARTWOOD-AI technology-transfer platform. The system integrates a Raspberry Pi 4 single-board computer with a Sony IMX500 intelligent camera and employs an interpretable computer-vision pipeline based on 21 handcrafted colour (HSV), texture (Gray-Level Co-occurrence Matrix and Local Binary Patterns), and edge-density (Canny) features classified using a Random Forest algorithm. A dataset comprising 24 beech boards (192 labelled image regions) was evaluated using a board-level train/validation partitioning strategy to prevent data leakage. The classifier achieved an average accuracy of 88.2% (±7.1%) during five-fold cross-validation and 82.5% accuracy on an independent validation set, with high sensitivity for defect detection (recall = 0.93, F1-score = 0.88). The results demonstrate the technical feasibility of the proposed embedded inspection architecture and establish a reproducible experimental baseline for future integration of deep-learning models, digital twins, and intelligent cutting optimisation within the SMARTWOOD-AI platform.

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