Wood Veneer Defect Detection Based on Multiscale DETR with Position Encoder Net

Ge, Yilin; Jiang, Dequan; Sun, Litao

doi:10.3390/s23104837

Cited by 2 publications

(1 citation statement)

References 51 publications

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“…Focusing on the tiny cracks, Lin et al [ 8 ] proposed a data-driven semantic segmentation network to recognize cracks at the pixel-level. Due to the limitation of the receptive field of CNN, Ge et al [ 23 ] introduced a detection transformer (DETR) to improve the detection performance. Unlike these methods, based on wood surface depth data, Xu et al [ 24 ] designed an improved Bi-LSTM network to identify the detective lines efficiently.…”

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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SiM-YOLO: A Wood Surface Defect Detection Method Based on the Improved YOLOv8

Xi,

Wang,

Liang

et al. 2024

Coatings

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Wood surface defect detection is a challenging task due to the complexity and variability of defect types. To address these challenges, this paper introduces a novel deep learning approach named SiM-YOLO, which is built upon the YOLOv8 object detection framework. A fine-grained convolutional structure, SPD-Conv, is introduced with the aim of preserving detailed defect information during the feature extraction process, thus enabling the model to capture the subtle variations and complex details of wood surface defects. In the feature fusion stage, a SiAFF-PANet-based wood defect feature fusion module is designed to improve the model’s ability to focus on local contextual information and enhance defect localization. For classification and regression tasks, the multi-attention detection head (MADH) is employed to capture cross-channel information and the accurate spatial localization of defects. In addition, MPDIoU is employed to optimize the loss function of the model to reduce the leakage of detection due to defect overlap. The experimental results show that SiM-YOLO achieves superior performance compared to the state-of-the-art YOLO algorithm, with a 9.3% improvement in mAP over YOLOX and a 4.3% improvement in mAP over YOLOv8. The Grad-CAM visualization further illustrates that SiM-YOLO provides more accurate defect localization and effectively reduces misdetection and omission issues. This study highlights the effectiveness of SiM-YOLO for wood surface defect detection and offers valuable insights for future research and practical applications in quality control.

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Wood Veneer Defect Detection Based on Multiscale DETR with Position Encoder Net

Cited by 2 publications

References 51 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

SiM-YOLO: A Wood Surface Defect Detection Method Based on the Improved YOLOv8

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