Automated Knot Detection and Pairing for Wood Analysis in the Timber Industry

TL;DR

This paper introduces a fully automated, machine learning-based pipeline for detecting and pairing knots in wood, significantly improving efficiency and accuracy in timber processing.

Contribution

It presents a novel automated system combining YOLOv8l detection and triplet neural networks for knot pairing, advancing automation in wood analysis.

Findings

YOLOv8l achieved an mAP@0.5 of 0.887 for knot detection.

Triplet neural network achieved 85% pairing accuracy.

Key features include distances from knot endpoints and longitudinal coordinates.

Abstract

Knots in wood are critical to both aesthetics and structural integrity, making their detection and pairing essential in timber processing. However, traditional manual annotation was labor-intensive and inefficient, necessitating automation. This paper proposes a lightweight and fully automated pipeline for knot detection and pairing based on machine learning techniques. In the detection stage, high-resolution surface images of wooden boards were collected using industrial-grade cameras, and a large-scale dataset was manually annotated and preprocessed. After the transfer learning, the YOLOv8l achieves an mAP@0.5 of 0.887. In the pairing stage, detected knots were analyzed and paired based on multidimensional feature extraction. A triplet neural network was used to map the features into a latent space, enabling clustering algorithms to identify and pair corresponding knots. The triplet network with learnable weights achieved a pairing accuracy of 0.85. Further analysis revealed that he distances from the knot's start and end points to the bottom of the wooden board, and the longitudinal coordinates play crucial roles in achieving high pairing accuracy. Our experiments validate the effectiveness of the proposed solution, demonstrating the potential of AI in advancing wood science and industry.