# A Comparative Analysis of Oak Wood Defect Detection Using Two Deep Learning (DL)-Based Software
## 1 Introduction  
Wood is a highly enduring construction material that has been utilized for millennia, as indicated by archaeological evidence of dwellings constructed more than 10,000 years ago, meaning wood played a key role as the principal resource for building materials. It is commonly recognized as a naturally renewable, long-lasting, ecologically and economically justifiable material, enabling its versatile application across multiple fields [1]. Furthermore, it exhibits heterogeneity, anisotropy, porosity, fibrous composition, and hygroscopic properties. It is also distinguished by structural imperfections such as live and dead knots, cracks, and discoloration. One of the primary objectives in the production process involves identifying and removing defects that can impact the final product's aesthetic, physical, and mechanical properties. During production, competent personnel essentially employ visual inspection techniques to remove natural defects [2, 3], which are vulnerable to human error and tend to be highly subjective, potentially impacting the general wood utilization rate. As a result, improving the efficiency of utilizing wood is has now been recognized as an active field of research in the scientific community in recent years [4, 5]. Enhancing the efficiency and accuracy of identifying wood defects on the wood surface has the potential to enhance wood utilization effectiveness and promote more rational wood consumption [6, 7, 8, 9]. Integrated digital image processing techniques and artificial intelligence algorithms are widely used for detecting and classifying wood knot defects [10]. Fixed feature extractors andcategorizing recognition technology have gained significant popularity and widespread utilization [11; 12]. The technology consists mainly of computer vision technology, spectral analysis technology, and numerous digital image processing techniques [13]. Significant improvements have been made in detecting wood defects over the past few years by merging computer technology with detection and control principles [14]. Deep learning (DL) represents a significant advancement in machine learning within the realm of computer vision. Utilizing DL, AI-driven assistants can assimilate information from user-provided images, offering solutions for a variety of image analysis tasks. Its primary advantage lies in its ability to tackle previously challenging tasks that were problematic for rule-based algorithms. However, with the rise of deep learning, the emphasis has shifted towards data handling, ensuring the quality of image annotations, and experimenting with various training parameters. These aspects now dominate much of the time dedicated to application development. Deep learning typically involves two primary stages: training and inference. During the training phase, a model is constructed by examining features gleaned from training data. The inference stage entails deploying this model to new images to execute machine vision tasks. Figure 1 shows the classic and machine learning approaches, consisting of training and inference modes. Deep learning (DL) techniques encompass anomaly detection, feature detection (segmentation), object classification, instance segmentation, point localization, and character recognition [15]. Over recent years, deep learning (DL) has surfaced as a prevalent computational approach in machine learning (ML), demonstrating remarkable outcomes in various intricate cognitive assignments, often comparable to or even surpassing human capabilities [16]. Derived from artificial neural networks (ANN), DL technology has garnered substantial attention in computing for its capacity to efficiently handle large volumes of data. This capacity to learn from extensive datasets is a notable advantage of deep learning [16]. In 2006, [PERSON] et al. [17] introduced the concept of \"deep learning\" (DL), which relied on the utilization of artificial neural networks (ANNs).