Improving consumer satisfaction with the appearance and surface quality of wood-based products requires inspection methods that are both accurate and efficient.The adoption of artificial intelligence(AI)for surface ev...Improving consumer satisfaction with the appearance and surface quality of wood-based products requires inspection methods that are both accurate and efficient.The adoption of artificial intelligence(AI)for surface evaluation has emerged as a promising solution.Since the visual appeal of wooden products directly impacts their market value and overall business success,effective quality control is crucial.However,conventional inspection techniques often fail to meet performance requirements due to limited accuracy and slow processing times.To address these shortcomings,the authors propose a real-time deep learning-based system for evaluating surface appearance quality.The method integrates object detection and classification within an area attention framework and leverages R-ELAN for advanced fine-tuning.This architecture supports precise identification and classification of multiple objects,even under ambiguous or visually complex conditions.Furthermore,the model is computationally efficient and well-suited to moderate or domain-specific datasets commonly found in industrial inspection tasks.Experimental validation on the Zenodo dataset shows that the model achieves an average precision(AP)of 60.6%,outperforming the current state-of-the-art YOLOv12 model(55.3%),with a fast inference time of approximately 70 milliseconds.These results underscore the potential of AI-powered methods to enhance surface quality inspection in the wood manufacturing sector.展开更多
This review aims to identify the assets and limitations of Dabema(Piptadeniastrum africanum)as a sustainable alternative to traditional timber species for furniture and construction applications.Dabema is characterize...This review aims to identify the assets and limitations of Dabema(Piptadeniastrum africanum)as a sustainable alternative to traditional timber species for furniture and construction applications.Dabema is characterized by its high density and dimensional stability,meeting ASTM(American Society for Testing and Materials)standards for mechanical strength,which is essential for promoting its use.However,its limited availability in trade and ingrained habits of use are obstacles to its widespread commercialization.In addition,thermal and oleothermal treatments have shown great potential for improving the characteristics of this wood,although they require ongoing optimization and rigorous environmental assessment.Consequently,increased awareness of the benefits of Dabema is decisive to encourage its sustainable adoption in modern economies.This could help to diversify forest resources and encourage more sustainable building practices,taking advantage of Dabema’s unique properties while mitigating environmental sustainability concerns.展开更多
文摘Improving consumer satisfaction with the appearance and surface quality of wood-based products requires inspection methods that are both accurate and efficient.The adoption of artificial intelligence(AI)for surface evaluation has emerged as a promising solution.Since the visual appeal of wooden products directly impacts their market value and overall business success,effective quality control is crucial.However,conventional inspection techniques often fail to meet performance requirements due to limited accuracy and slow processing times.To address these shortcomings,the authors propose a real-time deep learning-based system for evaluating surface appearance quality.The method integrates object detection and classification within an area attention framework and leverages R-ELAN for advanced fine-tuning.This architecture supports precise identification and classification of multiple objects,even under ambiguous or visually complex conditions.Furthermore,the model is computationally efficient and well-suited to moderate or domain-specific datasets commonly found in industrial inspection tasks.Experimental validation on the Zenodo dataset shows that the model achieves an average precision(AP)of 60.6%,outperforming the current state-of-the-art YOLOv12 model(55.3%),with a fast inference time of approximately 70 milliseconds.These results underscore the potential of AI-powered methods to enhance surface quality inspection in the wood manufacturing sector.
文摘This review aims to identify the assets and limitations of Dabema(Piptadeniastrum africanum)as a sustainable alternative to traditional timber species for furniture and construction applications.Dabema is characterized by its high density and dimensional stability,meeting ASTM(American Society for Testing and Materials)standards for mechanical strength,which is essential for promoting its use.However,its limited availability in trade and ingrained habits of use are obstacles to its widespread commercialization.In addition,thermal and oleothermal treatments have shown great potential for improving the characteristics of this wood,although they require ongoing optimization and rigorous environmental assessment.Consequently,increased awareness of the benefits of Dabema is decisive to encourage its sustainable adoption in modern economies.This could help to diversify forest resources and encourage more sustainable building practices,taking advantage of Dabema’s unique properties while mitigating environmental sustainability concerns.
