## 4 Results
### Bottle Identification and PIC Index Calculation Example
The 60% weighting assigned to the \(Rec_{Factor}\) emphasizes the role of visual recognition in object classification, yet the 40% weighting for the \(Cor_{Factor}\) serves as a critical check. In this instance, failing to meet the weight criteria and identifying the object as non-PET due to its characteristics results in a significantly reduced confidence score. There are instances when an object may weigh 20 or 30 g, but it is not a PET bottle. In such cases, we rely on the Azure Custom Vision service. For this reason, we will impose a minimum confidence threshold of 80% for the PBIA. When the _PIC_ index exceeds 80%, the object is classified as a PET item. Conversely, if the probability is 80% or lower, it is categorized as a non-PET object. The PBIA employs an ML model developed using 60 PET and 60 non-PET training images, which achieves a precision of 96% (Figure 9). Finally, 200 tests were conducted to validate this model, utilizing images distinct from those used during the training phase. Among the test images, 100 represented PET items, while the remaining 100 comprised non-PET objects. Notably, the non-PET category included scanned items that differed from those used in the training dataset. These tests were conducted automatically, and, for this purpose, a specialized C# script was developed to evaluate the PBIA's performance. Figure 16 displays a screenshot of the script's execution. The results were recorded in a file and subsequently analyzed to recalculate the accuracy, yielding a final value of 98% for the entire system (PET and non-PET images). The test results revealed that 22 out of 100 objects were incorrectly classified as non-PET items. In comparison, 4 out of 100 objects were mistakenly identified as PET items, despite  
Figure 11:2 Execution of C# script for performance evaluation of PBIA. being non-PET objects. A confusion matrix is presented in Figure 11:3 The model's 87% accuracy means its performance remains commendable, particularly given the challenging task of distinguishing between similar objects. The model's ability to accurately identify most of the PET items illustrates its effectiveness in the specific context of this application. Factual accuracy indicates how accurately the model identifies PET materials during testing, reflecting real-world performance. Specifically, it refers to the proportion of PET objects correctly classified as PET items. In this case, the model achieved 96% factual accuracy, meaning that only 4 out of 100 PET items were incorrectly classified as non-PET items, while the rest were correctly identified. This measurement demonstrates the model's effectiveness in distinguishing PET items, particularly when applied to the recycling identification task. The factual accuracy for identifying PET items stands at an impressive 96%, as only four elements were incorrectly classified as non-PET items. This high level of accuracy highlights the model's effectiveness in recognizing PET materials, reflecting its strong performance in distinguishing between recyclable and non-recyclable items. Factual accuracy in identifying PET materials is a reflection of how well the model performs in regard to recycling tasks. While a few PET items were mistakenly classified as non-PET items, the model's overall performance remains commendable. Specifically, with an actual accuracy rate of 96%, it is evident that the system is mainly effective in distinguishing between recyclable PET bottles and non-recyclable materials. The few misclassifications, in which four PET items were incorrectly categorized as non-PET items, illustrate the complexities inherent in object detection tasks.