Computer Vision-Based Wood Identification: A Review

Abstract

Wood identification is an important tool in many areas, from biology to cultural heritage. In the fight against illegal logging, it has a more necessary and impactful application. Identifying a wood sample to genus or species level is difficult, expensive and time-consuming, even when using the most recent methods, resulting in a growing need for a readily accessible and field-applicable method for scientific wood identification. Providing fast results and ease of use, computer vision-based technology is an economically accessible option currently applied to meet the demand for automated wood identification. However, despite the promising characteristics and accurate results of this method, it remains a niche research area in wood sciences and is little known in other fields of application such as cultural heritage. To share the results and applicability of computer vision-based wood identification, this paper reviews the most frequently cited and relevant published research based on computer vision and machine learning techniques, aiming to facilitate and promote the use of this technology in research and encourage its application among end-users who need quick and reliable results.

Once upon a Time, There Was a Piece of Wood: Present Knowledge and Future Perspectives in Fungal Deterioration of Wooden Cultural Heritage in Terrestrial Ecosystems and Diagnostic Tools

“…Identification is essential not only for understanding the original purpose of wood but also for effectively preserving WCH by distinguishing between authentic work and any subsequent repairs or alterations [8]. Macroscopic identification of woods is indeed challenging when dealing with historic artefacts [215], as many characteristics such as color, gloss, odor, weight, and structure are generally lost over time. It then becomes crucial to turn to microscopic identification.…”

Improving the competitiveness of aircraft manufacturing automated processes by a deep neural network

“…In particular, non contact artificial vision technologies should be flexible and scalable for those industrial applications that require high-precision operations in real uncontrolled environments [9][10][11]. These systems have reported good success rates in productive processes [12,13] and in other sectors [14][15][16]. For example, vision systems in the civil infrastructure field [17][18][19][20] have been successfully employed to capture images of large structures in real uncontrolled open environments.…”

Automatic resin duct detection and measurement from wood core images using convolutional neural networks

“…Specifically, deep learning (DL) and convolutional neural networks (CNN) have revolutionized wood technology in recent years by providing exceptional capabilities for processing large amounts of data, identifying patterns, and making predictions 9 , 10 . The CNN models have mainly been trained to identify wood species automatically 11 . With a motivation to preserve endangered species, prevent illegal logging, and ensure the authenticity of wood products, DL solutions have been proposed to recognize wood species from images of wood surface 11 – 17 , standing trees species recognition from 3D point clouds of trees collected by light detection and ranging (LiDAR) or terrestrial laser scanning (TLS) 18 – 20 and near-infrared (NIR) spectroscopy based tree species identification 21 , 22 .…”

“…The CNN models have mainly been trained to identify wood species automatically 11 . With a motivation to preserve endangered species, prevent illegal logging, and ensure the authenticity of wood products, DL solutions have been proposed to recognize wood species from images of wood surface 11 – 17 , standing trees species recognition from 3D point clouds of trees collected by light detection and ranging (LiDAR) or terrestrial laser scanning (TLS) 18 – 20 and near-infrared (NIR) spectroscopy based tree species identification 21 , 22 . Convolutional neural networks have also been proposed to process remote sensing and aerial images in order to monitor forest infestation and health conditions or detect fires 23 – 28 .…”