A large-scale image dataset of wood surface defects for automated vision-based quality control processes

Kodytek, Pavel; Bodzas, Alexandra; Bilík, Petr

doi:10.12688/f1000research.52903.2

Cited by 15 publications

(5 citation statements)

References 13 publications

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“…Our goal was to quickly assess the feasibility of the method. We chose the subset of the large-scale image dataset of wood surface defects (Kodytek et al, 2022), which was modified by Nouman Ahsan (Large Scale Image Dataset of Wood Surface Defects, 2023) This subset contains 4,000 labelled images of eight different categories of wood surface defects, such as live knots, dead knots, and knots with cracks. Taking advantage of the pre-labelled images in the dataset, we divided it into three parts: 81.25% for training, 12.5% for validation, and 6.25% for testing.…”

Section: First Results With Yolov8nmentioning

confidence: 99%

Towards Automatic Defects Analyses for 3d Structural Monitoring of Historic Timber

Chizhova,

Pan,

Luhmann

et al. 2024

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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Abstract. Stability of historic wooden constructions is changing with time and should be inspected appropriately for risk assessment and prevention. The stability or strength values of built-in historic timber are difficult or even impossible to be derived without invasive investigation, but this is particularly problematic for the monitoring of heritage objects. Luckily there are some visible timber surface features, like knots and cracks, which can act as individual evidence to estimate the wood strength as well as to adjust its grade class indicator. In the final project, we aim to compare different approaches for 3D digital documentation of historic wood timbers and focus on automatic knot detection using AI techniques. A first feasibility study reported here provides a scientific baseline for the development of an automated method to analyse historic timber stability using 3D surveying and recognised surface features. First results about texture and resolution properties are discussed here.

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Section: First Results With Yolov8nmentioning

confidence: 99%

Towards Automatic Defects Analyses for 3d Structural Monitoring of Historic Timber

Chizhova,

Pan,

Luhmann

et al. 2024

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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“…A dataset of 2200 images is used in this study, which is acquired from the literature, in particular the work of Kodytek et al [34]. Of these 2200 images, 2000 are equally categorized into the two classes: (i) "Single knot", and (ii) "Multiple knots", as illustrated in Figure 2, each set containing 1000 images.…”

Section: Division Of the Dataset And Methodology Stepsmentioning

confidence: 99%

Classification and Computing the Defected Area of Knots in Wooden Structures Using Image Processing and CNN

Ehtisham,

Qayyum,

Plevris

et al. 2023

15th International Conference on Evolutionary and Deterministic Methods for Design, Optimization and Control

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Knots in wooden structures are common natural features in wood that result from where branches once joined the trunk of a tree. While they can add to the aesthetic appeal of wood, knots are often considered structural defects in construction because they can significantly affect the mechanical properties of wood. If knots are present in structural members, they cannot be ignored. Identifying the presence of knots and finding the corresponding defected area of a structural member is important to be able to reinforce the member, compensate for the reduced strength and ensure that it is safe and suitable for its intended use. In this study, the Inception-ResNet-V2 pre-trained Convolutional Neural Network (CNN) model is trained and validated with 2000 images for the classification of knots, and the defected area is calculated through Image Processing (IP) and other soft computing techniques. The images of knots are collected and equally classified into two categories: 1000 "Single knot" and 1000 "Multiple knots" images. 70% of the dataset is used for training, and 30% for model validation. Four statistical parameters, namely accuracy, precision, recall, and F1 score, are calculated to check the model performance for the classification task, as well as the corresponding confusion matrix. The model exhibited an overall accuracy of 84% in an independent evaluation with a new testing dataset of 200 images, while the defects could be properly quantified using IP techniques.The research work shows the potential of AI-based methodologies in structural health monitoring and damage identification. These methods can drastically improve our ability to assess the condition of structures and structural elements, offering enhanced precision and accuracy, real-time and cost-effective monitoring, predictive capabilities, and automation opportunities.

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“…For the experiments, we utilized a large-scale image dataset of wood surface defects, which was generated by VSB-Technical University of Ostrava specifically for automated visual quality control processes [35]. This original dataset contains a total of 20,275 images, consisting of 1992 images without any defects and 18,283 images with one or more surface defects.…”

Section: Experimental Details and Datasetmentioning

confidence: 99%

ODCA-YOLO: An Omni-Dynamic Convolution Coordinate Attention-Based YOLO for Wood Defect Detection

Wang,

Liang,

Wang

et al. 2023

Forests

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Accurate detection of wood defects plays a crucial role in optimizing wood utilization, minimizing corporate expenses, and safeguarding precious forest resources. To achieve precise identification of surface defects in wood, we present a novel approach called the Omni-dynamic convolution coordinate attention-based YOLO (ODCA-YOLO) model. This model incorporates an Omni-dimensional dynamic convolution-based coordinate attention (ODCA) mechanism, which significantly enhances its ability to detect small target defects and boosts its expressiveness. Furthermore, to reinforce the feature extraction and fusion capabilities of the ODCA-YOLO network, we introduce a highly efficient features extraction network block known as S-HorBlock. By integrating HorBlock into the ShuffleNet network, this design optimizes the overall performance. Our proposed ODCA-YOLO model was rigorously evaluated using an optimized wood surface defect dataset through ablation and comparison experiments. The results demonstrate the effectiveness of our approach, achieving an impressive 78.5% in the mean average precision (mAP) metric and showing a remarkable 9% improvement in mAP compared to the original algorithm. Our proposed model can satisfy the need for accurate detection of wood surface defects.

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A large-scale image dataset of wood surface defects for automated vision-based quality control processes

Cited by 15 publications

References 13 publications

Towards Automatic Defects Analyses for 3d Structural Monitoring of Historic Timber

Towards Automatic Defects Analyses for 3d Structural Monitoring of Historic Timber

Classification and Computing the Defected Area of Knots in Wooden Structures Using Image Processing and CNN

ODCA-YOLO: An Omni-Dynamic Convolution Coordinate Attention-Based YOLO for Wood Defect Detection

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