## 4 Evaluation and Discussion
\begin{table}
\begin{tabular}{p{142.3 pt} p{142.3 pt} p{142.3 pt}} \hline \hline
**Article** & **Method** & **Results** \\ \hline [PERSON] et al. [55] & The authors focused on automatic inspection of wood knot defects. They presented a method that extracts image features from GLCM (gray level co-occurrence matrix) and classifies them using a feed-forward neural network (FFNN). ANN was optimized using a particle swarm optimization (PSO) algorithm. The authors used a relatively small database consisting of only 90 images of defective wood knots. The authors focused in the visual inspection of wood veneer defects. They proposed classifying statistical image features using multilayer ANN. The proposed ANN takes 17 features as inputs and classifies 13 different defect classes. The authors used a dataset of 232 examples of different defects. \\ [PERSON] et al. [57] & Damaged wood was chosen as the research object. The authors applied ANN and acoustic emission (AE) for damaged wood detection. Thirty different wood pieces were used in the research, from which 400 AE signals were generated. The authors applied the Hopfield neural network dynamic model for the detection of log boundaries. The experimental results showed that the proposed algorithm outputs accurate boundaries for the log. The method was applied on the digital log images acquired using the X-ray imaging system. The number of training and testing images is not specified. The authors presented a structure-aware crack defect detection scheme. It is based on two mathematical models, where the first one models a cracked surface, and the second one models a structured surface of a solar panel. The proposed method can detect a crack defect in a homogeneously textured background. The dataset consisted of more than 10,000 images. The authors focused on the detection of unpredictable fabric defects. They proposed an automatic fabric inspection method based on lattice segmentation and template statistics. The defect is detected using the lattice similarity value. The authors used a dataset consisting of 247 image samples of 256 x 256 pixels. The authors proposed a method based on the kernel correlation filter (KCF) target tracking algorithm for the detection of surface defects in moving particleboards on the production line. The authors applied CNN for defect detection on fruit surfaces. The proposed algorithm is sensitive to lighting conditions. The authors used a relatively small testing database consisting of only 120 test images. The authors applied computer vision method and CNN for defect detection in shearography images. Shearography is used to visualize areas of the pipes, which were repaired using glass fiber patches. The training and testing database consisted of 256 shearography images. The authors applied CNN for defect detection on different surfaces in the images, taken by unmanned aerial vehicles. Experiments were performed with 2500 images. The dataset consisted of four different background surfaces and exhibited differences in image lighting and motion blur. The authors applied fine-tuned CNNs for phenotype classification of zebrafish embryo. The proposed algorithm automatically classifies phenotype changes due to toxic substances. The authors applied deep CNNs for the diagnosis of glaucoma from eye fundus images. They had a dataset consisting of 1426 images (589 images of normal eye fundus and 837 images with glaucoma). \\ \hline \hline \end{tabular}  
\end{table} 
Table 5: Comparison of results.