## 4 Discussion  
The aerospace sector is one of the main technological and economic drivers that strengthens our present, constitutes our future and is a source of competitiveness and innovation with a huge capacity to catalyze talent, technological development and enormous power, transforming and generating new applications and services for our society's welfare and progress. Specifically, the objective of manufacturers with manufacturing and assembly lines is to automate the entire process by using digital technologies as part of the transition toward Industry 4.0 [4; 5; 13; 19]. Our research developed a detection neural network that analyzes and processes images in the most efficient way, simultaneously detecting and classifying all the fasteners that may be present during a manufacturing process in an industrial environment. In the aerospace industry, artificial vision techniques, combined with machine learning, have been used to identify defects and problems associated with the wear of engine components [38], assess corrosion in metallic systems [39], measure large panels by 3D systems [8] and inspect automated wheels [4]. However, they have not been employed to classify fastening elements of large structures with structural responsibility because of the technological difficulties entailing their practical application to manufacturing processes in an industrial environment. The results of this study suggest that the Patch Classifier software can successfully extract the characteristics of referencing elements. They also show that the convolutional neural network, programmed for classifying referencing elements, can obtain a success rate that comes close to or exceeds 70% in eight of the nine classes making up our network. If we consider the CNN in absolute element detection terms, the network's accuracy is more than 98% in an industrial environment. Our network presented sensitivities in all classes above 80%. In particular, it presented its best value (98.9 \(\pm\) 1.6%) for the screws class (S) and its worst value (84 \(\pm\) 14%) for the protruding rivet class (PR). The main differences between these two datasets are the geometry of the head of the fastening element and the proportion of both classes in relation to the total set of the validation sample. This suggests that these variables could influence sample specificity. However, our study did not exclude the possibility of other factors that could affect the network's result, such as a certain degree of similarity between the classes of straight and countersink drills (Table 2). The geometrical similarity in some of these classes was reflected in the network confusion matrix (Table 2). For the countersink and drill categories specifically, the matrix returned a value of 33.6%. Correspondingly, the matrix gave a confusion value of around 11% between the rivet and drill categories. This situation, however, did not occur between the hexagonal (Hx) category and other categories, as it presented a clearly different geometry. We believe that the poor accuracy in the countersink class (62.9 \(\pm\) 15.5%) was due to these elements' marked degree of similarity, particularly because of the circular shape. Therefore, this aspect could be considered a limitation of our study. High confusion values were also present among the screw, drill, countersink and rivet classes. This was probably caused by fewer images being available in the dataset for this class. This situation did not occur after a category regrouping was proposed (Tables 3 and 4), so we consider both these confusion values and the poor accuracy of this class (70.3 \(\pm\) 44.2%) to have been caused by an imbalance in the dataset regarding this class. This could also be considered a limitation of our study. In the aerospace industry, knowing if a hole is filled or empty based on subsequent machining operations during the manufacturing process is particularly interesting. This is why our classes were reorganized into three more generic categories (Table 3), with a success rate of 98.3% for the empty hole