Computer Vision-Based Wood Identification: A Review (original) (raw)

Wood Recognition and Quality Imaging Inspection Systems

Journal of Sensors

Forestry is an undoubtedly crucial part of today’s industry; thus, automation of certain visual tasks could lead to a significant increase in productivity and reduction of labor costs. Eye fatigue or lack of attention during manual visual inspections can lead to falsely categorized wood, thus leading to major loss of earnings. These mistakes could be eliminated using automated vision inspection systems. This article focuses on the comparison of researched methodologies related to wood type classification and wood defect detection/identification; hence, readers with an intention of building a similar vision-based system have summarized review to build upon.

A Survey on Wood Recognition Using Machine Vision

Wood recognition is an important issue that has been presented in many industrial enterprises such as the furniture industries and the wood panel production. Recently, research activities in this area have put the emphasis on the use of machine vision. Given one or more wood images, a wood recognition method can recognise or classify a wood sample into a certain wood specie. In this paper, the basic concepts and the development process of wood recognition have been introduced at first. Then, wood recognition methods were presented on three levels, i.e. wood image classification, method of feature extraction, classifier chooses. Finally, the future works in this area has been pointed out.

Improved Wood Species Identification Based On Multi-View Imagery of The Three Anatomical Planes

2022

Background: The identification of tropical African wood species based on microscopic imagery is a challenging problem due to the heterogeneous nature of the composition of wood combined with the vast number of candidate species. Image classification methods that rely on machine learning can facilitate this identification, provided that sufficient training material is available. Despite the fact that the three main anatomical sections contain information that is relevant for species identification, current methods only rely on the transversal section. Additionally, commonly used procedures for evaluating the performance of these methods neglect the fact that multiple images often originate from the same tree, leading to an overly optimistic estimate of the performance. Results: We introduce a new image dataset containing microscopic images of the three main anatomical sections of 77 Congolese wood species. A dedicated multiview image classification method is developed and obtains an ...

The applications of machine vision in raw material and production of wood products

BioResources

Machine vision has been developed nearly for 70 years and been widely applied in electronics, automotive manufacturing, food processing, etc. With deepening study of its theory and technology in forestry industry, the industry of wood products is moving steadily toward the goal of automated identification and production to improve the manufacturing intelligence of enterprises. In this study, theoretical and algorithmic research on image acquisition, feature extraction, recognition, and classification involved in machine vision-based wood recognition technology were analyzed on the basis of its global development. The applications of machine vision in the wood materials, such as the identification of tree species, wood inspection and classification, defects detection of wood product, surface analysis of wood color, and quality control of furnishing products were thoroughly analyzed. The development trend of machine vision in the production and management of wood materials was conside...

New Method of Recognition of Wood Species

Wood research

One of the qualitative features in machine vision, automation and sorting of wood is wood specie. Color in the form of words or terms of the seven lAWA 1989 qualitative classes such as reddish brown or yellowish gray has very small or even zero effectiveness in recognition wood species, as expressed by the percentage of correct decisions or discriminating power (DP). The aim of this work was to increase the efficiency of colorimetric recognition of wood species and discriminating functions (DP - discriminating power). This paper deals with the increasing of DP in recognition of two wood species similar in color: fu (Abies alba) and spruce (Picea excelsa). The three sets of measurements of color data on fu and spruce have been made: A: 300 measurements/5 stems/! wood specie of perfectly sound wood; B: 90 measurements/5 stems/1 wood specie of perfectly sound wood; C: 90 measurements/5 stems/1 wood specie, according to rigid measuring scheme inclusive small failures acceptable in class...

Wood Species Classification and Identification System

International Journal of Engineering Sciences & Research Technology, 2014

Automatic wood recognition has not yet been well established mainly due to lack of research in this area and the difficulty in obtaining the wood database. In this paper, an automatic wood recognition system based on image processing, feature extraction and artificial neural networks was designed. The proto-type PC-based wood recognition system is capable of classifying 30 different tropical Malaysian woods, according to their species based on the macroscopic wood anatomy. Image processing is carried out using our newly developed in-house image processing library referred to as “Visual System Development Platform”. The textural wood features are extracted using a co-occurrence matrix approach, known as grey-level co-occurrence matrix. A multi-layered neural network based on the popular back-propagation algorithm is trained to learn the wood samples for the classification purposes. The system can provide wood identification within seconds, eliminating the need for laborious human recognition. The results obtained show a high rate of recognition accuracy, proving that the techniques used are suitable to be implemented for commercial purposes.