## 4 Results
### Bottle Identification and PIC Index Calculation Example  
This subsection details two calculation scenarios. The first illustrates the identification of a PET bottle, and the other represents a non-PET object. Figure 12 illustrates how the Azure Custom Vision service scans and identifies objects as either PET or non-PET, with a weight of 20 g (Figure 13). The Azure Custom Vision service returns two PET identification tags with the following confidence levels, as depicted in Figure 12:  
Figure 11: Performance metrics of the Azure Custom Vision model trained on 100 images of PET items and 100 images of non-PET items. The system uses the highest confidence value, 99.9%, as the _RecFactor_. The _RecFactor_ is the confidence percentage that the Azure Custom Vision service returned for the PET tag with the highest confidence. In this case:  
\[RecFactor=0.999=99.9\%\]  
Next, the _CorFactor_ is computed based on the object's weight. The bottle weighs 20 g, within the acceptable tolerance range of \(\pm 2\) g from the standard weight of 20 g for an empty 500 mL PET bottle. Since the weight meets the criteria, the _CorFactor_ is set to 1. With \(o_{weight}=20\) g, \(\omega_{i}\in[20,30]\) grams, and \(\sigma=2\) g.  
\[Cor_{Factor}=1+3.7266\times 10^{-6}\approx 1\]  
Assuming \(\gamma=2\) and \(\delta=1\), the _PIC_ index is calculated using Equation (1):  
\[PIC=(0.6\times 0.999^{2}+0.4\times 1)\times 100=99.88\%\]  
The resulting _PIC_ index of 99.88% indicates high confidence that the object is a PET bottle. This is a direct result of the very high _RecFactor_ (99.9%) provided by the Azure  
Figure 12: Azure Custom Vision’s object identification as a PET bottle. Figure 13: Weight measurement of a PET bottle. Custom Vision service, as well as the validation of the object's weight as a PET bottle within the expected range. This example demonstrates the use of the _PIC_ index calculation to accurately identify PET bottles. The combination of Azure confidence and weight validation creates a highly reliable system, with the 99.94% _PIC_ index confirming that the object is undoubtedly a PET bottle and indicating that it has been identified as made from PET. In the second scenario, a non-PET object is analyzed. The example involves a glass bottle that presents significant complexities for the Azure Custom Vision model due to several factors. First and foremost, glass and PET bottles often share similar shapes, transparency, and sizes, which can lead to ambiguity in visual recognition. The resemblance in form can lead to the model misclassifying objects, especially when relying solely on visual characteristics. Figure 14 presents the object positioned on the scale for weight measurement, indicating 74 g. Figure 15 presents a screenshot from the Azure Custom Vision interface, displaying the identification results for the object, as follows:  
* Tag 1: 85% confidence;
* Tag 2: 26.3% confidence. The 85% confidence value from the Azure Custom Vision service arises because the glass object exhibits a similar shape and color to PET bottles. Figure 14: Weight measurement of a non-PET bottle. Figure 15: Azure Custom Vision’s object identification as a non-PET bottle. The \(Rec_{Factor}\) is determined according to the highest confidence value returned by the Azure Custom Vision service:  
\[Rec_{Factor}=0.85\]  
Next, the \(Cor_{Factor}\) is calculated based on the object's weight. The 74 g weight must be validated against the predefined standard weights for PET bottles. The acceptable standard weights for common PET bottle sizes (250 mL, 500 mL, and 750 mL) are 20 and 30 g. Since the weight of 74 g does not match either of these standard weights and exceeds the acceptable tolerance range of \(\pm\)2 g, the \(Cor_{Factor}\) is set to 0. Figure 11:0  
The _PIC_ index using Equation (1) is:  
Figure 11:1  
The resulting _PIC_ index of 43.35% indicates low confidence in identifying the object as a PET bottle. However, this is significantly lower than the previously calculated _PIC_ index for the PET example.