## 1 Introduction
[PERSON] and [PERSON] [16] used features obtained from Gabor filters and a fuzzy self-organizing neural network as a classifier to identify four different types of knots found on wood surfaces, achieving 91.17% accuracy. Traditionally, classical machine learning methods such as support vector machines (SVM) and K-nearest neighbor (KNN) have been used for the recognition of wood surface defects. [PERSON] et al. [17] used the intensity and size characteristics of these defects and applied SVM. [PERSON] et al. [18] identified knots using KNN, SVM, and texture descriptors. [PERSON] and [PERSON] [19] used a gray-level co-occurrence matrix (GLCM) and Tamura texture parameters for the classification of knots on wood surfaces. [PERSON] et al. [20] proposed using local binary patterns (LBP) and speeded-up robust features (SURF) for feature extraction, and SVM for the detection of cracks and knots in wood surface images. [PERSON] and [PERSON] [21] suggested using principal component analysis (PCA) or multidimensional scaling (MDS) with Mahalanobis distance for identifying wood vessels in wood hyperspectral microscopic images. Convolutional neural networks (CNN) are well suited for solving image classification problems, but they need a high number of samples for training. These networks are made up of several filters, whose outputs represent convolution and pooling operations. When images are analyzed using a sliding window, CNNs are very slow, because the object search time is dependent on the size of the image [22]. One of the solutions to this problem is to use regional matching methods, assuming that the searched visual objects have common visual features and stand out from the background of an image. Once a region with a high probability of finding an object is found, it is checked by using a more accurate classification method, thus determining its exact position and class. Using this method, the number of search regions is reduced considerably [22]. One of the major detectors of this type is region-based CNN (R-CNN) [23]. Faster R-CNN is an improved version of R-CNN with a faster training time [24]. In faster R-CNN, the image is processed using a series of sequentially connected convolution and maximum pooling layers, thereby producing a single map of the convolved features. This feature map selects the regions of interest (RoI) that are being processed in the pooling layers of the RoI. In this layer, each feature region is transformed into a vector of fixed size attributes, which are processed in fully connected network layers. After processing the region with a fully convoluted layer, the layer is branched into two separate output layers: the classification layer with softmax function is determined by the object class, and the positioning layer defines the positions of the rectangular points limiting the objects. Faster R-CNN has been used with success in medical image processing [25] and object tracking [26], but is still little used for the qualitative analysis of wood panel surfaces. Other types of neural network architectures have also been used for surface defect recognition. [PERSON] et al. [27] used a cascaded autoencoder (CASAE) to transform the surface image into a pixel-wise prediction mask. Then the surface regions are categorized into their respective classes using a compact CNN, achieving an accuracy of 89.60% for the metallic surface defect detection task. [PERSON] et al. [28] used PCA for the determination of the critical features before using ANN to identify wood veneer defects. [PERSON] et al. [29] used a pretrained decaf CNN [30] to generate a surface defect heatmap. Then the heatmap was binarized and segmented by adopting the graph-based Felzenszwalb's method. In [31], the VGG-16 network architecture achieved over 92% accuracy while allowing localization of the timber surface defects.