## 4 Results
### Testing Azure Custom Vision Service  
Figure 10 shows a screenshot of a plastic bottle from the training dataset. Thus, the bottle is placed in different positions on the sorting flap and oriented at various angles. The fact that a multitude of images must be acquired for each object, capturing it in various scenarios, represents a major drawback of the service used by ACVS. In real life, it is impossible to acquire all the features for all the objects in the world to perform correct training. Therefore, the model must be able to generalize and distinguish between objects based on this generalization, which must be distinctive between categories.  
Figure 11 shows a plastic bottle that is flattened. Suppose this image is sent to ACVS by placing the bottle in different positions and oriented at various angles (similar to the images in Figure 10). In that case, ACVS will not have the ability to identify that it is the same bottle.  
Figure 11: Test image of a crushed bottle classified in the plastic waste category.  
Figure 10: Screenshot of training images for the plastic waste category in ACVS.  
Figure 9: The CWSM uses unique word stats to differentiate among categories in the validation process.  
To recognize a flattened plastic bottle, ACVS needs to be trained with numerous bottles in different positions and at various angles. Moreover, using a flattened plastic bottle of a different color requires a separate dataset during the training phase to successfully perform the recognition. This behavior suggests the low capacity of the service regarding generalization, meaning the ability to extrapolate the features of objects identified during training. The degree of flattening of the bottle is another factor that affects the recognition capability. Essentially, if the bottle is flattened differently, there is a chance it will not be recognized correctly. Furthermore, another object from a different sorting category could be misidentified as a flattened plastic bottle if it shares more similarities with that object than with the training images in the plastic class.  
Table 2 presents the results for 400 unseen images after the training (which involved 1000 images, 250 for each category). Out of the one hundred tests for metal, ninety-eight were recognized as metal, one was identified as glass, and one was identified as plastic. For the cardboard tests, ninety-nine of one hundred were recognized as cardboard, while one was recognized as plastic. Ninety-seven were correctly identified for the glass tests, while two were identified as plastic and one as metal. For the plastic tests, sixty-seven were identified as plastic, eleven as metal, nineteen as glass, and three as cardboard (Figure 12).  
ACVS demonstrates moderate performance in classifying a never-seen object from a category. The confusion matrix is presented in Figure 12. The precision is calculated at 90.86%. In this context, the ACVS model avoids many false positives (FPs), meaning that it is often correct when it predicts a certain category. The accuracy of ACVS for never-before-seen objects is 95.13%.  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Category** & **Accuracy (\%)** & **Precision (\%)** & **Recall (\%)** & **F\({}_{1}\)-Score (\%)** \\ \hline Cardboard & 99.00 & 97.06 & 99.00 & 98.02 \\ Glass & 94.25 & 82.91 & 97.00 & 89.40 \\ Metal & 96.50 & 89.09 & 98.00 & 93.33 \\ Plastic & 90.75 & 94.37 & 67.00 & 78.36 \\ \hline Total & 95.13 & 90.86 & 90.25 & 90.55 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Performance parameters for each category of ACVS.  
Figure 12: Confusion matrix for cardboard, glass, metal, and plastic waste using ACVS.