Local Texture Representation for Timber Defect Recognition based on Variation of LBP

Rahiddin, Rahillda Nadhirah Norizzaty; Hashim, Ummi Rabaah; Salahuddin, Lizawati; Kanchymalay, Kasturi; Wibawa, Aji Prasetya; Chun, Teo Hong

doi:10.14569/ijacsa.2022.0131053

Cited by 3 publications

(3 citation statements)

References 25 publications

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“…In recent years, there has been a rise in quality control using automated vision inspection (AVI) among the manufacturer, particularly in the secondary timber industry with the objective to overcome present issues [3]. Although AVI has been applied in the timber industry to address these challenges, ongoing research endeavors persist in enhancing the inspection process across various domains, including defect detection and identification, characterizing defect, grading timber, and integrating sensors into hardware components for optimizing cutting processes [4]. A number of methods have been proposed [5][6] [7][8] [9] to streamline the grading process, yet they still encounter several obstacles especially in the scope of detection and identification of timber defects.…”

Section: Introductionmentioning

confidence: 99%

Timber Defect Identification: Enhanced Classification with Residual Networks

Chun,

Hashim,

Ahmad

2024

IJACSA

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This study investigates the potential enhancement of classification accuracy in timber defect identification through the utilization of deep learning, specifically residual networks. By exploring the refinement of these networks via increased depth and multi-level feature incorporation, the goal is to develop a framework capable of distinguishing various defect classes. A sequence of ablation experiments was conducted, comparing our proposed framework's performance (R1, R2 and R3) with the original ResNet50 architecture. Furthermore, the framework's classification accuracy was evaluated across different timber species and statistical analyses such as independent t-tests and one-way ANOVA tests were conducted to identify the significant differences. Results showed that while the R1 architecture demonstrated slight improvement over ResNet50, particularly with the addition of an extra layer ("ConvG"), the latter still maintained superior overall performance in defect identification. Similarly, the R2 architecture, despite achieving notable accuracy improvements, slightly lagged behind R1. Integration of fully pre-activation activation functions in the R3 architecture yielded significant enhancements, with a 14.18% increase in classification accuracy compared to ResNet50. The R3 architecture showcased commendable defect identification performance across various timber species, though with slightly lower accuracy in Rubberwood. Nonetheless, its performance surpassed both ResNet50 and other proposed architectures, suggesting its suitability for timber defect identification. Statistical analysis confirmed the superiority of the R3 architecture across multiple timber species and this underscores the significance of integrating network depth and fully preactivation activation functions in improving classification performance. In conclusion, while the wood industry has made strides towards automation in timber grading, significant challenges remain. Overcoming these challenges will require innovative approaches and advancements in image processing and artificial intelligence to realize the full potential of automated grading systems.

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Section: Introductionmentioning

confidence: 99%

Timber Defect Identification: Enhanced Classification with Residual Networks

Chun,

Hashim,

Ahmad

2024

IJACSA

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“…Furthermore, the presented visual classification scale could also be integrated with other technologies, such as image processing and artificial intelligence [40][41][42][43][44][45], to optimize wood production, assessment, and maintenance. Then, the classification could be made based on a visual observation in the field or based on image processing, either from pictures taken by humans or by pictures registered with unmanned vehicles.…”

Section: Introductionmentioning

confidence: 99%

Damage Assessment of Pine Wood Facades in the First Years of Service for Sustainable Maintenance

Almeida,

Delgado,

Labrincha

et al. 2023

Buildings

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The importance of the sustainability of wood buildings is increasing. The renewed attention highlights the need to assess the wood deterioration accurately, in the initial years of service, to optimize treatment during its lifetime and reduce maintenance costs. This study presents a methodology for inspecting and classifying damage of wood in service, relying on visual inspection and oriented to non-structural wooden components. This approach enables more affordable, widespread, and frequent monitoring of wooden elements in use, promoting their routine maintenance. The methodology was tested in the pine wood (Pinus sylvestris) facades with up to 5 years of service in a hotel building in northern Portugal. Despite its relatively brief period of operation, the building displays indications of both abiotic and biotic degradation of the wood across all its different facade orientations. Based on that, the study highlights the natural aging of Scots pine according to the building’s age, orientation, maintenance treatments, and exposure conditions. These findings provide insights into conservation plan optimization and emphasize the need for regular maintenance of wooden elements in outdoor environments, even in the early years of service.

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“…Feature sets 3 and 4 contain LBP and SIFT features extracted from each of the feature subsets described in feature set 1. The parameters of LBP were set to radius = 1, sample points = 8, and method = uniform, following the recommendations by Rahillda et al [82] based on their experimental results. The SIFT parameters used in this experiment were set according to the guidelines by Lowe [80]: nFeatures = max, nOctaveLayers = 3, contrastThreshold = 0.3, edgeThreshold = 10, and sigma = 1.6.…”

mentioning

confidence: 99%

Efficient Retrieval of Images with Irregular Patterns Using Morphological Image Analysis: Applications to Industrial and Healthcare Datasets

Zhang,

Cosma,

Bugby

et al. 2023

J. Imaging

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Image retrieval is the process of searching and retrieving images from a datastore based on their visual content and features. Recently, much attention has been directed towards the retrieval of irregular patterns within industrial or healthcare images by extracting features from the images, such as deep features, colour-based features, shape-based features, and local features. This has applications across a spectrum of industries, including fault inspection, disease diagnosis, and maintenance prediction. This paper proposes an image retrieval framework to search for images containing similar irregular patterns by extracting a set of morphological features (DefChars) from images. The datasets employed in this paper contain wind turbine blade images with defects, chest computerised tomography scans with COVID-19 infections, heatsink images with defects, and lake ice images. The proposed framework was evaluated with different feature extraction methods (DefChars, resized raw image, local binary pattern, and scale-invariant feature transforms) and distance metrics to determine the most efficient parameters in terms of retrieval performance across datasets. The retrieval results show that the proposed framework using the DefChars and the Manhattan distance metric achieves a mean average precision of 80% and a low standard deviation of ±0.09 across classes of irregular patterns, outperforming alternative feature–metric combinations across all datasets. Our proposed ImR framework performed better (by 8.71%) than Super Global, a state-of-the-art deep-learning-based image retrieval approach across all datasets.

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Local Texture Representation for Timber Defect Recognition based on Variation of LBP

Cited by 3 publications

References 25 publications

Timber Defect Identification: Enhanced Classification with Residual Networks

Timber Defect Identification: Enhanced Classification with Residual Networks

Damage Assessment of Pine Wood Facades in the First Years of Service for Sustainable Maintenance

Efficient Retrieval of Images with Irregular Patterns Using Morphological Image Analysis: Applications to Industrial and Healthcare Datasets

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