Improved Hough Transform and Total Variation Algorithms for Features Extraction of Wood

Du, Weiwei; Xi, Yarui; Harada, Kiichi; Zhang, Yumei; Nagashima, Keiko; Zhang, Qiao

doi:10.3390/f12040466

Cited by 8 publications

(4 citation statements)

References 8 publications

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“…At present, researchers in the field of wood defect detection have focused their research on the detection of surface images of wood defects and defect detection has entered the stage of informatization development. In recent years, more and more researchers have applied computer image processing and other technologies to the field of wood defect detection, forming a series of new feature detection methods [7]. In the 1980s, Gao et al used a specific thresh-old to divide the grayscale image of wood into rectangular blocks and realized the detection of wood defects based on texture analysis and spatial correlation of the image [8].…”

Section: Literature Reviewmentioning

confidence: 99%

A Wood Quality Defect Detection System Based on Deep Learning and Multicriterion Framework

Sun

2022

Journal of Sensors

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In order to solve the problems of image perception and quality decision-making of wood defects with typical bionic intelligent algorithms, the existence of multidimensional degradation factors causes serious real problems of image distortion; the author proposes a wood defect image reconstruction and quality evaluation model based on deep reinforcement learning. The model introduced the deep learning mechanism and realized real-time and efficient reconstruction of multidimensional defect images of different wood by using the deep residual network for iterative training. In this model, a panoramic autonomous perception model was constructed for the fine segmentation and feature extraction of multidimensional defects of different wood and a shared resource pool of wood defect features of the magnitude of big data was constructed. Introduce the reinforcement learning mechanism, use the deep deterministic policy gradient algorithm, and establish a high-dimensional decision mapping between the iterative update of defect features, autonomous decision-making, panoramic visualization, depth prediction, and wood quality evaluation; it realizes the horizontal sharing integration of multidimensional difference wood defect image reconstruction and quality evaluation. The results obtained are as follows: in a typical environment, systematic wood quality evaluation, and autonomous intelligent decision-making performance, the coincidence rate with artificial defect recognition and evaluation efficiency can reach 90% and the loss of the training set can be controlled below 0.2%. Compared with the traditional wood quality grading system, the wood defect image reconstruction, and quality evaluation model system designed by the author, the quality evaluation decision-making efficiency rate was 90.19%, an increase of about 20%, and the system decision-making operation and maintenance loss was 2.23%, a decrease of about 10%. It is proved that the system designed by the author can realize the timely detection of wood quality defects very effectively and save a lot of manpower and material resources.

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Section: Literature Reviewmentioning

confidence: 99%

A Wood Quality Defect Detection System Based on Deep Learning and Multicriterion Framework

Sun

2022

Journal of Sensors

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“…The application of deep learning models not only enables accurate detection of timber end face contours and diameter measurement but also demonstrates excellent scalability. For instance, extracting tree rings from timber end faces, closely linked to the timber's strength, can serve as one of the criteria for timber sorting [30]. Defect detection on timber end faces, affecting the strength and texture of the timber, is another potential application [31].…”

Section: Prospectsmentioning

confidence: 99%

Design and Implementation of an Intelligent Log Diameter Grading and Sorting Line Based on Machine Vision

Ding,

Gong,

Kong

et al. 2024

Forests

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In order to address the challenges posed by elevated manual labor costs and limited automation in traditional log diameter grading and sorting processes, this paper centers on the design and research of an intelligent log diameter grading and sorting line utilizing machine vision. The study focuses on logs with smaller diameters located in Fujian province, China. By analyzing production requirements, the study formulates the structure of the feeding, alignment, detection, and sorting zones to fulfill sorting functions. Using the YOLOv5 model, the system achieves accurate log end face positioning, and the diameter is computed through a designated algorithm. The operational process of the system is examined, and the control logic governing the production line is elucidated. Evaluating the practical performance of the production line, the study assesses the accuracy of diameter recognition, precision in grading, and operational efficiency. The results reveal that the absolute error in diameter detection for the sorting line averages 1.12 mm, with sorting accuracy exceeding 95%. The sorting line can automatically categorize logs with diameters ranging from 60 mm to 300 mm and lengths ranging from 2 m to 6 m, achieving an annual sorting capacity of 120,000 to 130,000 cubic meters. The research findings illustrate that the system fulfills the industry’s demands for log diameter grading and sorting, thereby enhancing economic efficiency for enterprises.

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“…The threshold can be set by the user or automatically selected using the algorithm [35,38]. To determine straight lines for the which correspond to the rails in the images, we used the Hough transform method [39,40]. Each point of the boundary is represented as (x, y) in the image, for which possible parameters (angle and/or radius) are generated that define a geometric shape that passes through this point, such as a line or a circle (in our case, a line), i.e., an 'accumulation matrix' or 'accumulation space' is created, in which each possible set of parameters (angle, radius, etc.)…”

Section: 𝑧 𝑧 𝑧mentioning

confidence: 99%

The Detection of Railheads: An Innovative Direct Image Processing Method

Tverdomed,

Dmytro,

Kokriatska

et al. 2024

Sustainability

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This study presents a fully automated railhead detection method based on a direct image processing algorithm for use on a railway track. This method functions at a much faster pace than artificial intelligence algorithms that process rail images on embedded systems or low-power devices, as it does not require the use of significant computing resources. With the use of this method, railheads can be analyzed to identify the presence of cracks and other defects. We converted color images to halftone images, performed histogram equalizations to improve the contrast, applied a Gaussian filter to reduce the presence of noise, utilized convolutional filters to extract any vertical and horizontal lines, applied the Canny method and Sobel filters to refine the boundaries of the extracted lines, applied the Hough transform technique to extract lines belonging to the railhead images, and identified the segments with the highest brightness values to process the images of the railheads under study. The method of railhead separation described in this article will allow for further comprehensive diagnostics of the condition of rail threads to ensure the safe and sustainable operation of railway transport. The implementation of intelligent maintenance systems and effective monitoring of railway track conditions can reduce the negative impact on the environment and contribute to the advancement of rail transport as a sustainable, safe, and more environmentally friendly mode of transportation.

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Improved Hough Transform and Total Variation Algorithms for Features Extraction of Wood

Cited by 8 publications

References 8 publications

A Wood Quality Defect Detection System Based on Deep Learning and Multicriterion Framework

A Wood Quality Defect Detection System Based on Deep Learning and Multicriterion Framework

Design and Implementation of an Intelligent Log Diameter Grading and Sorting Line Based on Machine Vision

The Detection of Railheads: An Innovative Direct Image Processing Method

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