An innovative method for log diameter measurements based on deep learning

The widespread adoption of Deep Learning techniques for Computer Vision in recent years has brought major changes to the world of industry, contributing greatly to this sector's transition to Industry 4.0, also referred to as Smart Industry. This involves an increasingly predominant role of machines and automation within industrial processes. In this context, the Swedish forest industry is an excellent context for applying these techniques. In particular, this work will deal with automating the measurement of log diameters to date carried out manually by operators in the industry. The proposed methodology will use two object detection neural networks, one deputed to detect logs in the scene and the other for the calibrated target. The latter thus allows the camera calibration to be fully automated, enabling each diameter to be measured without any further operations by the operator. The results obtained are satisfactory and open the way for the industrial application of the proposed methodology.