Search Results (13)

Structural timber is an important building material, but surface defects such as cracks and knots seriously affect its load-bearing capacity, dimensional stability, and long-term durability, posing a significant risk to structural safety. Conventional inspection methods are unable to address the issues of multi-scale defect characterization, inter-class confusion, and morphological diversity, thus limiting reliable construction quality assurance. To overcome these challenges, this study proposes WDNET-YOLO: an enhanced deep learning model based on YOLOv8n for high-precision defect detection in structural wood. First, the RepVGG reparameterized backbone utilizes multi-branch training to capture critical defect features (e.g., distributed cracks and dense clusters of knots) across scales. Second, the ECA attention mechanism dynamically suppresses complex wood grain interference and enhances the discriminative feature representation between high-risk defect classes (e.g., cracks vs. knots). Finally, CARAFE up-sampling with adaptive contextual reorganization improves the sensitivity to morphologically variable defects (e.g., fine cracks and resin irregularities). The analysis results show that the mAP50 and mAP50-95 of WDNET-YOLO are improved by 3.7% and 3.5%, respectively, compared to YOLOv8n, while the parameters are increased by only 4.4%. The model provides a powerful solution for automated structural timber inspection, which directly improves building safety and reliability by preventing failures caused by defects, optimizing material utilization, and supporting compliance with building quality standards. Full article

Air-coupled ultrasound overcomes the limitations of traditional contact-based ultrasonic methods that rely on liquid couplants. Still, it faces challenges due to the acoustic impedance mismatch between air and wood, causing significant signal scattering and attenuation. This results in weak transmission signals contaminated by clutter and noise, compromising measurement accuracy. This study proposes a coded pulse air-coupled ultrasonic method for detecting defects in wood. The method utilizes Golay code complementary sequences (GCCSs) to generate excitation signals, with its feasibility validated through mathematical analysis and simulations. A-scan imaging was performed to analyze the differences in signal characteristics between defective and non-defective areas, while C-scan imaging facilitated a quantitative assessment of defects. Experimental results demonstrated that GCCS-enhanced signals improved the ultrasonic penetration and axial resolution compared to conventional multi-pulse excitation. The method effectively identified defects such as knots and pits, achieving a coincidence area of 85% and significantly enhancing the detection accuracy. Full article

To overcome the problems of the low signal-to-noise ratio and poor performance of wood ultrasonic images caused by ring-down vibrations during the ultrasonic quality detection of wood, a composite pulse excitation technique using a wood air-coupled ultrasonic detection system is proposed. Through a mathematical analysis of the output of the ultrasonic transducer, the conditions necessary for implementing composite pulse excitation were analyzed and established, and its feasibility was verified through COMSOL simulations. Firstly, wood samples with knot and pit defects were used as experimental samples. We refined the parameters for the composite pulse excitation technique by conducting A-scan measurements on both defective and non-defective areas of the samples. Moreover, two stepper motors were employed to control the path for C-scan imaging to detect wood defects. The experiment results showed that the composite pulse excitation technique significantly enhanced the precision of nondestructive ultrasonic testing for wood defects compared to the traditional single-pulse excitation method. This technique successfully achieved precise detection and location of pit defects, with a detection accuracy rate of 90% for knot defects. Full article

Solid wood is renowned as a superior material for construction and furniture applications. However, characteristics such as dead knots, live knots, piths, and cracks are easily formed during timber’s growth and processing stages. These features and defects significantly undermine the mechanical characteristics of sawn timber, rendering it unsuitable for specific applications. This study introduces BDCS-YOLO (Bilateral Defect Cutting Strategy based on You Only Look Once), an artificial intelligence bilateral sawing strategy to advance the automation of timber processing. Grounded on a dual-sided image acquisition platform, BDCS-YOLO achieves a commendable mean average feature detection precision of 0.94 when evaluated on a meticulously curated dataset comprising 450 images. Furthermore, a dual-side processing optimization module is deployed to enhance the accuracy of defect detection bounding boxes and establish refined processing coordinates. This innovative approach yields a notable 12.3% increase in the volume yield of sawn timber compared to present production, signifying a substantial leap toward efficiently utilizing solid wood resources in the lumber processing industry. Full article

Guided by the principles of visual perception and basic design, this research was conducted to examine users’ preferences on how they perceive natural unique wood phenomena of Pedunculate Slavonian Oak veneers (Quercus robur L.) such as color changes, wood rays, sapwood, and knots, which in the production and technical sense represent defects and wood residue, but could be used in the design of sustainable and ecological wall decorations. The goal was to detect samples with the most positive attributes and to observe their connection with activities and functional space if they are viewed as wall coverings in the interior. The results confirm that discoloration and wood rays are considered the most harmonious (“prettiest”) decors. Discoloration is associated with quiet cognitive or medium-intensity activity that can be used in libraries, hotel rooms, and classrooms. Wood rays are connected with very quiet activity in ambulance waiting rooms or religious buildings and museums. Knot decors are considered the most natural and are recommended for interiors with very or medium-intensive activities such as restaurants, cafes, and hotel receptions, but attention should be paid to the way the wall decors are arranged on the walls. Sapwood–hardwood decors are the subject of further research and improvement concerning the relationship between the dark and light parts of the veneer. In conclusion, the results provide useful guidelines for manufacturers with a large veneer residue in production and who want to design decorative wall panels, as well as for designers and architects designing interiors for a specific purpose and function where certain user behavior and psychological stimulation are desired. Full article

This article presents the operation of an automatic pine sawn timber inspection system, which was developed at the Woodinspector company and is offered commercially. The vision inspection system is used to detect various wood defects, including knots, blue stain, and mechanical damage caused by worms. A blue stain is a defect that is difficult to detect based on the color of the wood, because it can be easily confused with wood defects or dirt that do not impair its strength properties. In particular, the issues of detecting blue stain in wood, the use of artificial neural networks, and improving the operation of the system in production conditions are discussed in this article. While training the network, 400 boards, 4 m long, and their cross-sections of 100 × 25 [mm] were used and photographed using special scanners with laser illuminators from four sides. The test stages were carried out during an 8-hour workday at a sawmill (8224 m of material was scanned) on material with an average of 10% blue stain (every 10th board has more than 30% of its length stained blue). The final learning error was assessed based on defective boards detected by humans after the automatic selection stage. The system error for 5387 boards, 550 m long, which had blue staining that was not detected by the scanner (clean) was 0.4% (25 pieces from 5387), and 0.1 % in the case of 3412 boards, 610 mm long, on which there were no blue stains, but were wrongly classified (blue stain). For 6491 finger-joint boards (180–400 mm), 48 pieces were classified as class 1 (clean), but had a blue stain (48/6491 = 0.7%), and 28 pieces did not have a blue stain, but were classified as class 2 (28/3561 = 0.7%). Full article

The world’s expanding population presents a challenge through its rising demand for wood products. This requirement contributes to increased production and, ultimately, the high-quality and efficient utilization of basic materials. Detecting defects in wood elements, which are inevitable when working with a natural material such as wood, is one of the difficulties associated with the issue above. Even in modern times, people still identify wood defects by visually scrutinizing the sawn surface and marking the defects. Industrial scanners equipped with software based on convolutional neural networks (CNNs) allow for the rapid detection of defects and have the potential to accelerate production and eradicate human subjectivity. This paper evaluates the suitability of defect recognition software in industrial scanners against software specifically designed for this task within a research project conducted using Adaptive Vision Studio, focusing on feature detection techniques. The research revealed that the software installed as part of the industrial scanner is more effective for analyzing knots (77.78% vs. 70.37%), sapwood (100% vs. 80%), and ambrosia wood (60% vs. 20%), while the software derived from the project is more effective for analyzing cracks (70% vs. 65%), ingrown bark (42.86% vs. 28.57%), and wood rays (81.82% vs. 27.27%). Full article

This paper reports on investigations of the air-coupled ultrasonic (ACU) method to detect common defects in solid timber panels made of Chinese fir (Cunninghamia lanceolata (Lamb.) Hook.). The ACU technology is a non-contact method for nondestructive timber testing with quicker scanning rates compared to contact methods. A testbed was set up consisting of commercially available piezo-ceramic ACU transducers and in-house manufactured signal processing circuits. To demonstrate the suitability of the ACU technique, through-transmission measurement results are presented for samples with defects such as knots, wormholes, and cracks. Pulse compression methods (Barker-coded method) were used to improve the power of received signals based on cross-correction algorithms. Results showed defects of timber panels made of Chinese fir can be detected with a thickness of less than 40 mm. Defects larger than 3 mm in diameter could be detected with high precision. Applying the pulse compression method showed better results than using common sine signals as excitation signals since it increased the signal-to-noise ratio, which is especially important for air-coupled measurement of high-attenuation materials like timber materials. The measurement results on reference samples demonstrated that ACU technology is a promising method for timber defect detection, especially for the quality assessment of engineered wood products. Full article

Defects and cracks in dried natural timber (relative permittivity 2–5) may cause structural weakness and enhanced warping in structural beams. For a pine wood beam (1200 mm × 70 mm × 70 mm), microwave reflection (S₁₁) and transmission (S₂₁) measurements using a cavity-backed slot antenna on the wood surface showed the variations caused by imperfections and defects in the wood. Reflection measurements at 4.4 GHz increased (>5 dB) above a major knot evident on the wood surface when the E-field was parallel to the wood grain. Similar results were observed for air cavities, independent of depth from the wood surface. The presence of a metal bolt in an air hole increased S₁₁ by 2 dB. In comparison, transmission measurements (S₂₁) were increased by 6 dB for a metal screw centered in the cavity. A kiln-dried pine wood sample was saturated with water to increase its moisture content from 17% to 138%. Both parallel and perpendicular E-field measurements showed a difference of more than 15 dB above an open saw-cut slot in the water-saturated beam. The insertion of a brass plate in the open slot created a 7 dB rise in the S₁₁ measurement (p < 0.0003), while there was no significant variation for perpendicular orientation. By measuring the reflection coefficient, it was possible to detect the location of a crack through a change in its magnitude without a noticeable change (<0.01 GHz) in resonant frequency. These microwave measurements offer a simple, single-frequency non-destructive testing method of structural timber in situ, when one or more plane faces are accessible for direct antenna contact. Full article

Due to the lack of forest resources in China and the low detection efficiency of wood surface defects, the output of solid wood panels is not high. Therefore, this paper proposes a method for detecting surface defects of solid wood panels based on a Single Shot MultiBox Detector algorithm (SSD) to detect typical wood surface defects. The wood panel images are acquired by an independently designed image acquisition system. The SSD model included the first five layers of the VGG16 network, the SSD feature mapping layer, the feature detection layer, and the Non-Maximum Suppression (NMS) module. We used TensorFlow to train the network and further improved it on the basis of the SSD network structure. As the basic network part of the improved SSD model, the deep residual network (ResNet) replaced the VGG network part of the original SSD network to optimize the input features of the regression and classification tasks of the predicted bounding box. The solid wood panels selected in this paper are Chinese fir and pine. The defects include live knots, dead knots, decay, mildew, cracks, and pinholes. A total of more than 5000 samples were collected, and the data set was expanded to 100,000 through data enhancement methods. After using the improved SSD model, the average detection accuracy of the defects we obtained was 89.7%, and the average detection time was 90 ms. Both the detection accuracy and the detection speed were improved. Full article

In recent years, due to the shortage of timber resources, it has become necessary to reduce the excessive consumption of forest resources. Non-destructive testing technology can quickly find wood defects and effectively improve wood utilization. Deep learning has achieved significant results as one of the most commonly used methods in the detection of wood knots. However, compared with convolutional neural networks in other fields, the depth of deep learning models for the detection of wood knots is still very shallow. This is because the number of samples marked in the wood detection is too small, which limits the accuracy of the final prediction of the results. In this paper, ResNet-34 is combined with transfer learning, and a new TL-ResNet34 deep learning model with 35 convolution depths is proposed to detect wood knot defects. Among them, ResNet-34 is used as a feature extractor for wood knot defects. At the same time, a new method TL-ResNet34 is proposed, which combines ResNet-34 with transfer learning. After that, the wood knot defect dataset was applied to TL-ResNet34 for testing. The results show that the detection accuracy of the dataset trained by TL-ResNet34 is significantly higher than that of other methods. This shows that the final prediction accuracy of the detection of wood knot defects can be improved by TL-ResNet34. Full article

Wood is widely used in construction, the home, and art applications all over the world because of its good mechanical properties and aesthetic value. However, because the growth and preservation of wood are greatly affected by the environment, it often contains different types of defects that affect its performance and ornamental value. To solve the issues of high labor costs and low efficiency in the detection of wood defects, we used machine vision and deep learning methods in this work. A color charge-coupled device camera was used to collect the surface images of two types of wood from Akagi and Pinus sylvestris trees. A total of 500 images with a size of 200 × 200 pixels containing wood knots, dead knots, and checking defects were obtained. The transfer learning method was used to apply the single-shot multibox detector (SSD), a target detection algorithm and the DenseNet network was introduced to improve the algorithm. The mean average precision for detecting the three types of defects, live knots, dead knots and checking was 96.1%. Full article

Portable spectroscopic instruments are an interesting alternative for in-field and on-line measurements. However, the practical implementation of visible-near infrared (VIS-NIR) portable sensors in the forest sector is challenging due to operation in harsh environmental conditions and natural variability of wood itself. The objective of this work was to use spectroscopic methods as an alternative to visual grading of wood quality. Three portable spectrometers covering visible and near infrared range were used for the detection of selected naturally occurring wood defects, such as knots, decay, resin pockets and reaction wood. Measurements were performed on wooden discs collected during the harvesting process, without any conditioning or sample preparation. Two prototype instruments were developed by integrating commercially available micro-electro-mechanical systems with for-purpose selected lenses and light source. The prototype modules of spectrometers were driven by an Arduino controller. Data were transferred to the PC by USB serial port. Performance of all tested instruments was confronted by two discriminant methods. The best performing was the microNIR instrument, even though the performance of custom prototypes was also satisfactory. This work was an essential part of practical implementation of VIS-NIR spectroscopy for automatic grading of logs directly in the forest. Prototype low-cost spectrometers described here formed the basis for development of a prototype hyperspectral imaging solution tested during harvesting of trees within the frame of a practical demonstration in mountain forests. Full article