The segmentation of timber defects based on color and the mathematical morphology

Chen, Lijun; Wang, Keqi; Xie, Ying; Wang, Jincong

doi:10.1016/j.ijleo.2013.07.098

Cited by 6 publications

(3 citation statements)

References 14 publications

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“…Traditional detection approaches can be divided into the following three categories: threshold-based approaches, statistical-based approaches, and model-based approaches. The threshold-based approaches generally combine color difference and mathematical morphology [ 10 ], setting a suitable threshold [ 11 ] to segment the defective region in the local [ 12 ] or global [ 3 ] ranges. For the statistical-based approaches, which usually adopt the local binary pattern (LBP) [ 13 , 14 ] and gray level co-occurrence matrix (GLCM) [ 15 ] to measure the distribution of pixel values so as to achieve a classification result in the image level.…”

Section: Related Workmentioning

confidence: 99%

A Multi-Source Data Fusion Network for Wood Surface Broken Defect Segmentation

Zhu,

Xu,

Lin

et al. 2024

Sensors

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Wood surface broken defects seriously damage the structure of wooden products, these defects have to be detected and eliminated. However, current defect detection methods based on machine vision have difficulty distinguishing the interference, similar to the broken defects, such as stains and mineral lines, and can result in frequent false detections. To address this issue, a multi-source data fusion network based on U-Net is proposed for wood broken defect detection, combining image and depth data, to suppress the interference and achieve complete segmentation of the defects. To efficiently extract various semantic information of defects, an improved ResNet34 is designed to, respectively, generate multi-level features of the image and depth data, in which the depthwise separable convolution (DSC) and dilated convolution (DC) are introduced to decrease the computational expense and feature redundancy. To take full advantages of two types of data, an adaptive interacting fusion module (AIF) is designed to adaptively integrate them, thereby generating accurate feature representation of the broken defects. The experiments demonstrate that the multi-source data fusion network can effectively improve the detection accuracy of wood broken defects and reduce the false detections of interference, such as stains and mineral lines.

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Section: Related Workmentioning

confidence: 99%

A Multi-Source Data Fusion Network for Wood Surface Broken Defect Segmentation

Zhu,

Xu,

Lin

et al. 2024

Sensors

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show abstract

“…Unfortunately, this idea often leads to several incorrect regions because of non-homogeneity of the wood surface (Ramos and Rezaei 2010). Furthermore, selecting the appropriate primary region is a major problem in such approaches (Chen et al 2014). A filtering concept has been widely used in some methods for separating defects from other areas of wood surfaces.…”

Section: Introductionmentioning

confidence: 99%

“…Furthermore, selecting the appropriate primary region is a major problem in such approaches (Chen et al . 2014). A filtering concept has been widely used in some methods for separating defects from other areas of wood surfaces.…”

Section: Introductionmentioning

confidence: 99%

A new method for defect detection in lumber images: optimising the energy model by an irregular parametric genetic approach

Shojaedini

Haghighi

Kermani

2017

International Wood Products Journal

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Accurate detection of surface defects plays a vital role in automated analysis of lumber quality in the wood industries. A new method, based on a genetic optimisation of energy model, is introduced here for defect detection in lumber images. In this method, a hypothesis testing framework is defined, first to separate defects from natural tissue of lumber. Then, the boundary of lumber is estimated by a decision function based on the energy optimisation method which is driven by an irregular parametric genetic approach. Performance of the proposed algorithm was evaluated on real captured lumber images containing several types of surface defects. The results demonstrate that the proposed method extracts the defects approximately 4.4% better than its alternatives while at the same time decreasing false detections by approximately 5.4%. The results obtained also show considerable improvements in accuracy and precision of the proposed method compared to other examined methods, especially when a low false detection (i.e. at least 10%) is desired.

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Deep Learning Based Defect Inspection Using the Intersection Over Minimum Between Search and Abnormal Regions

Choi

Kim

2020

Int. J. Precis. Eng. Manuf.

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The segmentation of timber defects based on color and the mathematical morphology

Cited by 6 publications

References 14 publications

A Multi-Source Data Fusion Network for Wood Surface Broken Defect Segmentation

A Multi-Source Data Fusion Network for Wood Surface Broken Defect Segmentation

A new method for defect detection in lumber images: optimising the energy model by an irregular parametric genetic approach

Deep Learning Based Defect Inspection Using the Intersection Over Minimum Between Search and Abnormal Regions

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