A fast and robust artificial intelligence technique for wood knot detection

Abstract

This study reports the feasibility of using deep convolutional neural networks (CNN), for automatically detecting knots on the surface of wood with high speed and accuracy. A limited dataset of 921 images were photographed in different contexts and divided into 80:20 ratio for training and validation, respectively. The “You only look once” (YoloV3) CNN-based architecture was adopted for training the neural network. The Adam gradient descent optimizer algorithm was used to iteratively minimize the generalized intersection-over-union loss function. Knots on the surface of wood were manually annotated. Images and annotations were analyzed by a stack of convolutional and fully connected layers with skipped connections. After training, model checkpoint was created and inferences on the validation set were made. The quality of results was assessed by several metrics: precision, recall, F1-score, average precision, and precision x recall curve. Results indicated that YoloV3 provided knot detection time of approximately 0.0102 s per knot with a relatively low false positive and false negative ratios. Precision, recall, f1-score metrics reached 0.77, 0.79, and 0.78, respectively. The average precision was 80%. With an adequate number of images, it is possible to improve this tool for use within sawmills in the forms of both workstation and mobile device applications.