Machine vision technology for the forest products industry

Conners, Richard W.; Kline, D. Earl; Araman, Philip A.; Drayer, Thomas H.

doi:10.1109/2.596626

Cited by 18 publications

(9 citation statements)

References 3 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…The optical method has been recognized as a promising technique and some optical systems have been developed to detect wood surface features. Recently, the color line scan camera was used to automatically optimize crosscut and to sort red oak edge panel parts (Conners et al 1997) and the color feature histograms were used to separate wood defects into eight categories (Koivo and Kim 1989). However, the appearance of wood varies greatly and there are no two boards or defects that have the same properties of color or texture.…”

Section: Introductionmentioning

confidence: 99%

Automatic detection of sound knots and loose knots on sugi using gray level co-occurrence matrix parameters

Xiao

Yun

et al. 2011

Annals of Forest Science

View full text Add to dashboard Cite

& Context Knowledge of the occurrence of sound and loose knots on the surface of sawn sugi (Cryptomeria japonica L.f.) is important for its grading and application. & Objectives This study examined an optical system for detecting sound and loose knots in sugi instead of human being using the combining information of the color and texture features. & Methods The proposed system could be conceptually divided into two components: a CCD camera scanning system and a defect detecting algorithm developed by the authors. In the algorithm, the contrast parameter calculated from a graylevel co-occurrence matrix was used to locate the potential defects represented by sound knots and loose knots. The rulebased approach, which was built according to the color feature histograms, was used to identify sound knots and loose knots. A series of samples containing single or multiple sound and/or loose knots were selected at random to verify the efficiency and accuracy of the proposed system. & Results There were 94 sound knots and 86 loose knots on the surfaces of these samples, and the accuracy of locating the positions of sound knots and loose knots was 94.7% and 97.6%, respectively. The accuracies of identifying knots as sound or loose were 96.6% and 98.8%, respectively. The overall detection accuracy of the system was 93.9%. & ConclusionsThe results indicate that the proposed vision system is an efficient means of detecting sound knots and loose knots.

show abstract

Section: Introductionmentioning

confidence: 99%

Automatic detection of sound knots and loose knots on sugi using gray level co-occurrence matrix parameters

Xiao

Yun

et al. 2011

Annals of Forest Science

View full text Add to dashboard Cite

show abstract

“…Vision-based lumber scanning systems exist and are being increasingly adopted by the industry [20,10]. With prices for such systems below $1,000,000, they seem to be an attractive investment for medium to large size rough mills.…”

Section: Discussionmentioning

confidence: 99%

“…By quantifying the potential yield increases, one can investigate the economics of automated scanning systems for reducing or eliminating yield losses due to operator error. Such vision-based lumber defect detection systems are becoming commercially available [10]. They recognize defective areas of boards with a high accuracy and thus allow computerbased yield optimization and saw control systems to efficiently use the clear areas of a board.…”

Section: Rough Millmentioning

confidence: 99%

Impact of human error on lumber yield in rough mills

Buehlmann

Thomas

2002

Robotics and Computer-Integrated Manufacturing

View full text Add to dashboard Cite

Rough sawn, kiln-dried lumber contains characteristics such as knots and bark pockets that are considered by most people to be defects. When using boards to produce furniture components, these defects are removed to produce clear, defect-free parts. Currently, human operators identify and locate the unusable board areas containing defects. Errors in determining a defect and its location, known as operator error, lead to lower lumber yield and increased product cost. Technology exists that would alleviate these problems and is a viable option to avoid wasting lumber because' of human error. This study was performed in a rough mill collecting data on the errors made by humans when marking defects. Computer-based simulation tools were used to assess the significance of these errors. It was found that three-quarters of the decisions made by human operators are erroneous in some way resulting in an absolute yield loss of approximately 16.1 %. Thus, automated defect detection systems that perform more accurately than do humans could have a payback period of 1 year or less. Published by Elsevier Science Ltd.

show abstract

“…Cada etapa constitui um diferente contexto, possui diferentes níveis de complexidade e envolve conhecimentos específicos, além dos inerentes ao domínio da aplicação. Já as influências compreendem a subjetividade do especialista humano e as características do processo (repetitivo, monótono e demorado), com a sobrecarrega do profissional, possíveis distrações e baixas taxas de acerto (CONNERS et al, 1997;PHAM et al, 1997;RADOVAN et al, 2001).…”

Section: Introductionunclassified

Sistema computacional para identificar níveis de degradação em barras de aço

Santos¹,

Gavião²,

Chapla³

et al. 2021

Congresso Internacional Sobre Patologia E Reabilitação Das Construções

View full text Add to dashboard Cite

Reinforcement corrosion in concrete structures is one of the most important pathological manifestations that leads to degradation and structural damage of buildings, while represents risks to people lives. Analysis and identification of degradation levels of these structures is characterized as a repetitive, monotonous and time-consuming process. In this scenario, computer systems based on computer vision have been applied to similar problems to replace or assist human specialists. This paper presents a proposal to classify different degradation levels in steel bars through computer vision. A widely known set of tools in the machine learning area was used and validated the proposal. The best results achieved recognition rates of 75.6% by using LPQ descriptor and SVM machine learning algorithm.

show abstract

Machine vision technology for the forest products industry

Cited by 18 publications

References 3 publications

Automatic detection of sound knots and loose knots on sugi using gray level co-occurrence matrix parameters

Automatic detection of sound knots and loose knots on sugi using gray level co-occurrence matrix parameters

Impact of human error on lumber yield in rough mills

Sistema computacional para identificar níveis de degradação em barras de aço

Contact Info

Product

Resources

About