## 6 Conclusions  
The cloud service obtained the best results in natural illumination conditions, with good prediction confidence and sufficiently few errors, ensuring object picking in every case. The implementation proved its overall connectivity consistency, while also demonstrating robustness, providing good results in the case of partially dirty objects.  
The prediction confidence calculated by the Custom Vision and MVTec Merlic are not directly comparable since the methods of calculation of the confidence are not transparent for either of them. Per our observation, after making additional tests, the confidence of MVTec Merlic for cases of correct prediction tends to be closer to the extremes (either very high, which happens for most cases, or in specific cases, very low); we suspect that this behavior stems from the method used for calculating the confidence.  
While the specialized software showed better and more consistent results overall, which is to be expected since it is a computer vision specifically made with industrial use in mind, the Custom Vision solution also performed satisfactorily. These results open additional application perspectives in the robotics field with quickly evolving AI technology and continuously increasing cloud capabilities.  
It is worth mentioning that Custom Vision's lower limit of images for the training to take place is 25, and the results compared were obtained for a trained model where we used 40 training images. We observed that for further training of the Custom Vision model with new images of the objects, its prediction confidence kept increasing. The point after which the confidence is not further increased by adding new images is yet to be determined.  
Despite the revolutionary potential of cloud computing, this technology also comes with certain limitations on aspects such as security, flexibility, or performance caused by Internet dependency. Further research needs to be conducted on this topic to ensure the safety of data while optimizing performance, which could benefit not only individual end-users, but also enterprises that are using these types of services.  
In the future, a system that combines classical object identification, based on contours and edges, with NN-based detection, based on the two systems that are compared in this work, can be developed to account for all the detection scenarios, in terms of light, background, overlap, and dust, to obtain the best overall results.  
Conceptualization, I-L.S. and A.M.; methodology, I-L.S., A.M. and S.R.; software, I-L.S., A.M. and I.L.; validation, I-L.S., A.M. and S.R.; formal analysis, I-L.S. and A.M.; investigation, I-L.S. and A.M.; resources, I-L.S., F.D.A. and S.R.; data curation, A.M.; writing--original  
Figure 19: (**a**) Confidence histogram for natural light after retrain. (**b**) Confidence histogram for artificial light after retraining.  
draft preparation, I.-L.S. and A.M.; writing--review and editing, I.-L.S., A.M., I.L., S.R., F.D.A., D.C.P. and I.S.S.; visualization, A.M.; supervision, S.R., D.C.P. and I.S.S.; project administration, A.M.; funding acquisition, F.D.A., I.-L.S. and A.M. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the National Program for Research of the National Association of Technical Universities GNac ARUT 2023, grant number 83/11.10.2023 (optDDNc).  
Dataset available on request from the authors.  
The authors declare no conflicts of interest.