## 4 Results
### Architectural Differences and Real-Time Performance Comparision  
ACVS is a pre-trained service configured for image classification. It uses a CNN to categorize an image into a specific category. The programmer declares these categories, but the networks are not customized, except by manually adding image labels. The ACVS service automatically processes distinctive features between images belonging to different categories. Table 5 presents the key comparative elements between the two models. In contrast to ACVS, the CWSM has a hybrid approach. The first stage analyzes the current image and performs descriptive extraction through GVAS. The description consists of semantic characteristics. In the second stage, the CWSM processes and assigns the textual  
Figure 19: CWSM probability that the object belongs to each category in the second scenario. \begin{table}
\begin{tabular}{c c c} \hline
**Metric** & **ACVS** & **CWSM** \\ \hline Classification type & CNN & Hybrid \\ \hline Methodology & Pattern identification & Text feature extraction \\ \hline Object processing & Key pattern distinctive category extraction & Features comparison among categories \\ \hline Flexibility & Extended training dataset & High generalization on a reduced dataset \\ \hline \end{tabular}
\end{table} 
Table 5: Key Comparative elements between ACVS and the CWSM. description to one of the four defect categories in the training phase and then predicts the category based on the validation description. ACVS is an architecture suitable for applications specific to controlled environments, i.e., when the testing images are similar to the training ones. A concrete example is the one disseminated by the authors in [58], where defect detection at the level of gear wheels is performed. In this case, the images for identifying the defect significantly differ from the training images. On the other hand, in the case of a basket, the images have too much variability. This variability causes a system to be unable to generalize to such an extent that it can accurately identify all objects. ACVS trains using the following procedures:  
* Load all images;
* Mark in all images the area containing the object and associate the label with the corresponding category of the object;
* Train the model. Analyzing these steps of the training procedure reveals that the programmer lacks flexibility in developing the model. On the other hand, this procedure is advantageous from the perspective of relieving the programmer of the burden of implementing image processing mechanisms and from the perspective of the Microsoft service's capability for auto-tuning model parameters to generate the best metrics. GVAS has a superior generalization capability to ACVS, as it uses semantic descriptions rather than pixel-level analyses. Unlike ACVS, GVAS is more efficient in real-time processing because it does not require an extensive database of images for comparison. Text-based processing is much faster due to reduced time-space complexity. Moreover, the CWSM, including GVAS as a first layer, has higher accuracy than ACVS, even for unknown objects, as it classifies based on descriptive characteristics, not just visual ones. This behavior demonstrates the superior generalization ability of the CWSM compared to ACVS. One final comparative advantage is the enhanced adjustability of the GVAS model in terms of optimizations. This is possible by improving the labels provided by GVAS, given that the first component of the model is text based. Therefore, the CWSM performs better in real-time applications due to its reduced inference time, high generalization capability, and ability to improve under varying conditions. ACVS must compare each provided image with the training dataset. This complex process makes the ACVS model slow and dependent on cloud infrastructure. In contrast, the CWSM classifies based on semantic characteristics, resulting in reduced processing time. The ACVS model cannot recognize objects it did not see during training.