## 4 Results  
The position of the objects, which are compared in this section, represents the center of the bounding box containing the objects, as seen in Figure 7, and is also the picking point for handling the object. A screenshot of the bounding boxes and tags for the object that were identified using the industrial solution is presented in Figure 8. The tool for identification is called \"Deep Learning AI - Find Objects\" and uses an NN computed by the MVTec Deep Learning Tool, trained in an analogous manner to the cloud solution, using the same 40 images of the objects, for which bounding boxes and tags were assigned. In Tables 1 and 2, ten experiments are presented for each system, five experiments conducted under natural light and five under artificial light, with one of each type of object. The detection confidence is displayed as provided by the vision system; the errors represent the position difference in millimeters in the vision plane from the placing point, as used by the industrial robot, and the pick point returned by the vision system. Finally, the time  
Figure 8: Detected objects, as seen in MVTec Merlic. Figure 7: The picking point, calculated as the center of the bounding box. represents the duration of image processing in seconds or milliseconds, from the moment the image is sent to the vision system to the moment the results are received. In Tables 3 and 4 the mean values and SD are illustrated, in the same conditions as those previously obtained for each system. For this analysis, twelve experiments were performed for each object, placed in distinct positions and under both types of illumination, totaling 120 experiments. In Table 3, it can be observed that the lowest detection confidence, when using the cloud service, is obtained for the rhombus, under artificial light, with a mean of 0.63 and an SD of 0.13, while the best detection confidence is gained for the naturally illuminated circle, with a mean of 0.93 and an SD of 0.03. In terms of positioning error, the biggest error is seen when detecting the trapezoid, having a mean error on the X axis of 3.00 mm with an SD of 2.19 mm, and a mean error on the Y axis of 3.13 mm with an SD of 1.35 mm. The smallest errors can be seen in the detection of the circle, with errors of 0.53 mm on the X axis and 0.61 mm on the Y axis, along with SDs of 0.37 mm on the X axis and 0.53 mm on the Y axis. A similar case can be seen in Table 4, for the industrial application, with the smallest errors for the detection of the circle (0.66 mm mean error on the X axis and 0.4 mm SD and 0.48 mm mean error on the Y axis and 0.29 mm SD), and the biggest error for the trapezoid (3.05 mm mean and 1.69 mm SD on X axis, and 2.89 mm mean and 1.86 SD on Y axis). The confidence in this case varies extraordinarily little, with almost all cases having a confidence of 1, except for the square and trapezoid, which has reduced values when illuminated by artificial light. Regarding the response time, for both systems, the results are consistent and do not vary much with the object type or ambient light. In the case of the cloud system, the response time varies from a mean of 2.55 s and an SD of 0.14 s in the case of the artificially illuminated hexagon, to a mean of 2.94 s and an SD of 0.39 s for the naturally illuminated circle. The industrial software offers a response in about 500 ms in all cases, with the lowest average value being for the artificially lightened square (mean of 472.6 ms and SD of 27.7 ms) and the highest mean value for the naturally lightened trapezoid (mean of 564.6 ms and SD of 37.3 ms). Using the data extracted from the same experiments, histograms for confidence (Figures 9 and 10), error on 'X' axis (Figures 11 and 12), error on 'Y' axis (Figures 13 and 14) and time (Figures 15 and 16) have been plotted for both systems. It can be observed that the mean and mode vary heavily for the plotted histograms, which denotes that the distributions are negatively skewed for the confidence, and positively skewed for the errors, due to their limits.