Detection of mechanical damages in sawn timber using convolutional neural networks

Abstract

Quality control of timber products is a vital issue for the sawmill industry. One of the laborious parts of this control is to search mechanical damages in boards. Automation of this process decreases the time needed for board inspection and, therefore, improves the overall quality of timber products. Distinguishing barely noticeable mechanical damage from high contrast natural wood textures is a challenging problem. This study aimed to develop a robust and efficient method for recognition and localization of various mechanical damages on the images of sawn timber. Convolutional neural networks (CNN) was selected as a method to determine the existence of the mechanical damages on parts of the board images. In the proposed method the patches are extracted from the image of the board and classified with the CNN. The defects are localized based on the CNN prediction and coordinates of the patches. In this study four existing CNN architectures, namely AlexNet, GoogLeNet, VGG-16, and ResNet-50 were tested for mechanical damages detection. The VGG-16 architecture achieved the best results with over 90% classification accuracy for the image patches and 58% average defect localization accuracy.