Image Reconstruction of Internal Defects in Wood Based on Segmented Propagation Rays of Stress Waves

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

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.

SGN-YOLO: Detecting Wood Defects with Improved YOLOv5 Based on Semi-Global Network

Object detection based on wood defects involves using bounding boxes to label defects in the surface image of the wood. This step is crucial before the transformation of wood products. Due to the small size and diverse shape of wood defects, most previous object detection models are unable to filter out critical features effectively. Consequently, they have faced challenges in generating adequate contextual information to detect defects accurately. In this paper, we proposed a YOLOv5 model based on a Semi-Global Network (SGN) to detect wood defects. Unlike previous models, firstly, a lightweight SGN is introduced in the backbone to model the global context, which can improve the accuracy and reduce the complexity of the network at the same time; the backbone is embedded with the Extended Efficient Layer Aggregation Network (E-ELAN), which continuously enhances the learning ability of the network; and finally, the Efficient Intersection and Merger (EIOU) loss is used to solve the problems of slow convergence speed and inaccurate regression results. Experimental results on public wood defect datasets demonstrated that our approach outperformed existing target detection models. The mAP value was 86.4%, a 3.1% improvement over the baseline network model, a 7.1% improvement over SSD, and a 13.6% improvement over Faster R-CNN. These results show the effectiveness of our proposed methodology.

The Strength of Pine (Pinus sylvestris L.) Sawn Timber in Correlation with Selected Wood Defects

Pine timber of Polish origin intended for structural purposes is characterized by significant variability in the quality parameters. Technological suitability determined on the basis of relevant international classifications is based on the assessment of both selected mechanical and physical properties of wood. Moreover, the description of visual properties is also a valuable indicator regarding defect distribution. In the group of quality features playing a crucial role in the classification of sawn timber, there are knots, disruptions of grains, cracks, etc. Thus, the aim of the research was to determine the correlation between the presence of selected defects and the strength properties of individual timber pieces. This type of study is based on a nondestructive test method that allows for high optimization of sawn materials processing. In the case of sawn timber of Polish origin, the modulus of elasticity (MOE) determined using the sonic test is commonly used as a criterion. The research material was harvested from southern Poland. The results of the conducted studies confirmed a correlation between an increasing occurrence of particular types of defects and the results of MOE. Furthermore, as a result of the performed investigations, no significant effect of narrow surface cracks on strength properties was observed.

Defect Detection of Industry Wood Veneer Based on NAS and Multi-Channel Mask R-CNN

Wood veneer defect detection plays a vital role in the wood veneer production industry. Studies on wood veneer defect detection usually focused on detection accuracy for industrial applications but ignored algorithm execution speed; thus, their methods do not meet the required speed of online detection. In this paper, a new detection method is proposed that achieves high accuracy and a suitable speed for online production. Firstly, 2838 wood veneer images were collected using data collection equipment developed in the laboratory and labeled by experienced workers from a wood company. Then, an integrated model, glance multiple channel mask region convolution neural network (R-CNN), was constructed to detect wood veneer defects, which included a glance network and a multiple channel mask R-CNN. Neural network architect search technology was used to automatically construct the glance network with the lowest number of floating-point operations to pick out potential defect images out of numerous original wood veneer images. A genetic algorithm was used to merge the intermediate features extracted by the glance network. Multi-Channel Mask R-CNN was then used to classify and locate the defects. The experimental results show that the proposed method achieves a 98.70% overall classification accuracy and a 95.31% mean average precision, and only 2.5 s was needed to detect a batch of 50 standard images and 50 defective images. Compared with other wood veneer defect detection methods, the proposed method is more accurate and faster.

Lignocellulosic Composites from Acetylated Sunflower Stalks

Sunflower stalks could be an alternative raw material for use in the particleboard industry since the requirements of P1 general purpose boards for use in dry conditions and P2 boards for interior fitment for use in dry conditions are easily satisfied. Acetylation of sunflower stalks is found to greatly improve the thickness swelling (TS) value, with acetylated boards showing 19.7% weight gain, meeting the TS requirements of P3 Non-Load-Bearing—Humid and P4 Load Bearing—Dry criteria; however, acetylation adversely affects the internal bond strength (IBS). It is suggested that combinations of industrial wood chips with sunflower raw material may be used for the overall improvement performance of the particleboards.