Performance Analysis Using CNN for Detecting Wood Knots

This study proposes a Convolutional Neural Network (CNN) model to quickly and accurately detect wood deformations. The performance of the CNN was enhanced by extracting structural deformation features, optimizing training parameters, and improving datasets. Experimental analyses demonstrate that the CNN achieved high accuracy rates and is an effective method for deformation detection. The CNN model was designed to identify various wood deformations. Its layered architecture was optimized to analyze deformations at different scales and levels of detail. Minimal preprocessing was applied to the images used during training, and data augmentation techniques were employed to enhance dataset diversity. The model was trained on a training dataset and tested on a validation dataset. Metrics such as loss function and accuracy were monitored throughout the training process. The CNN achieved an accuracy rate of 99.90% on the training dataset. This study highlights that the CNN model is an effective method for non-destructive detection of wood deformations. The proposed CNN model has potential applications in wood deformation detection and quality control processes.