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 two-stage pipeline for knot detection and pairing. In the detection stage, high-resolution surface images of wooden boards are collected using industrial-grade cameras, and a large-scale dataset is manually annotated and preprocessed. After the transfer learning, the YOLOv8l achieves an mAP@0.5 of 0.887. In the pairing stage, we define and extract a set of multidimensional features from detected knots. A triplet neural network is used to map the features into a latent space, enabling clustering algorithms to identify and pair corresponding knots. The triplet network with learnable weights achieve a pairing accuracy of 0.85. Further analysis reveals that the 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.