# A Comparative Analysis of Oak Wood Defect Detection Using Two Deep Learning (DL)-Based Software
## 1 Introduction
Subsequently, deep learning emerged as a prominent subject, leading to a resurgence in the study of neural networks, thus giving rise  
Figure 1: Schematic illustration of the classic approach where the algorithm is designed manually and the machine learning approach where a training set of labeled images is required [15]. to the designation of \"new generation neural networks\". The recent prominence of deep learning technology in machine learning, artificial intelligence, data science, and analytics is attributed to its capacity to acquire knowledge from available data. Deep learning (DL) is often regarded as a component of both machine learning (ML) and artificial intelligence (AI). As a result, DL can be viewed as an AI mechanism that mimics the data-processing abilities of the human brain [16]. The algorithms used in deep learning benefit from the augmentation of data generation, enhanced computing capabilities, and the expanding availability of artificial intelligence (AI) as a service. Despite the presence of diverse, unorganized, and interconnected data sources, deep learning empowers robots to address complex problems effectively. The effectiveness of deep learning algorithms improves as they acquire more knowledge and experience [18; 19; 20; 21]. Convolutional neural networks (CNNs), recurrent neural networks (RNNs), denoising autoencoders (DAEs), deep belief networks (DBNs), and long short-term memory (LSTM) networks represent some of the most commonly utilized deep learning techniques, enjoying considerable popularity and extensive adoption. This section provides a comprehensive overview of each method, including a detailed description and an exploration of significant applications [22]. The convolutional neural network (CNN) is well recognized as a prominent framework within the deep learning (DL) field, mainly employed for image processing tasks. The architecture consists of three distinct layers, each incorporating various convolutional, pooling, and fully connected layers [22]. The most common convolutional neural network (CNN) designs are ZFNet [23], GoogLeNet [24], VGGNet [25], AlexNet [26], and ResNet [27]. A convolutional neural network (CNN) is an approach that employs an artificial neural network featuring a trainable architecture comprised of numerous layers [28] (Figure 2). Convolutional neural networks (CNNs) offer numerous benefits when applied to image processing tasks [15]; the integration of feature extraction and classification inside a single network structure allows for synchronized training, resulting in an entirely adaptable method. As the image size increases, the extraction of deep feature information is improved. The unique network topology of the system exhibits robust adaptability to various local deformations, image rotations, image translations, and other alterations present in the input image. In the present investigation, the representation of wood was subjected to convolution while simultaneously examining the characteristics of defects. [PERSON] et al. [29] utilized a mixed, fully convolutional neural network (Mix-FCN) for the automatic detection and categorization of wood flaws using photographs of wood surfaces. Nevertheless, the utilization of computer resources by the Mix-FCN algorithm was excessive for all wood specimens, irrespective of the fact that a significant proportion of these specimens were defect-free, resulting in time inefficiency. The approach suggested by [PERSON] et al. [30] involved utilizing a convolutional neural network (CNN) for the automated visual inspection of defects on wood product surfaces. Although this algorithm has shown effectiveness in detecting wood defects, it has yet to be implemented in practical applications. [PERSON] et al. [31] employed a pre-trained ResNet152 neural network model in combination with the faster R-CNN to identify surface flaws on wood panels. The researchers reported a high accuracy rate of 96.1%.