## 4 Results and Discussions  
In this section, we present an analysis of the outcomes achieved by our trained models, namely YOLOv5n and YOLOv7, as assessed on a validation dataset comprising 525 images containing a total of 1818 instances. Our evaluation process encompasses various performance metrics, including Precision, Recall, F1 score, mean Average Precision at IoU threshold 0.5 (mAP50), and mean Average Precision within the IoU range of 0.5 to 0.95 (mAP50-95). By employing these metrics, we thoroughly assess the effectiveness and accuracy of our models in object detection tasks, providing a comprehensive evaluation of their performance. The precision metrics of two models, YOLOv5n and YOLOv7, were evaluated and demonstrated in Figures 14 and 15. These metrics provide important insights into the models' performance in accurately predicting object classes and minimizing false positives. The optimal weights for both models resulted in a remarkable precision score of 0.972 across all classes. Precision is a measure of how accurately the model predicts the positive classes, and a score of 0.972 indicates that the models accurately predicted over 97% of the true positive classes. In addition to high precision, the models also exhibited an extremely low number of false positives. Furthermore, the models' performance was compared to previous research [26,32,33]. The models outperformed previous work by achieving higher precision. This indicates that the models have improved upon existing methods and are more effective in accurately detecting and classifying objects in the weighing scales context. To provide a more detailed breakdown of the precision values for individual classes in YOLOv5n and YOLOv7, Tables 2 and 3 were presented, respectively. These tables provide a comprehensive view of how well each class is predicted by the models. By examining the precision values for individual classes, it is possible to identify any specific classes that may pose challenges for the models or classes that the models excel at detecting. We calculated precision metrics by employing standard equations given below:  
\[P=\frac{TP}{TP+FP}\]  
Figure 13: The entire process of weighing, detection, and price prediction. The recall metrics for two models, YOLOv5n and YOLOv7, were evaluated and compared. The results are depicted in Figures 16 and 17, respectively. Both models achieved high recall values, with YOLOv5n achieving a recall of 0.988 and YOLOv7 achieving a recall of 0.986 across all classes. Recall is a metric used to evaluate the performance of object detection models. It measures the ability of a model to correctly identify positive instances out of all the actual positive instances. Based on the provided recall values, it can be inferred that both versions of YOLO (YOLOv5n and YOLOv7) were able to identify over 98% of the true positive instances, indicating a high level of performance. Additionally, the results indicate that there were very few false negatives, meaning that the models successfully minimized the instances where positive objects were missed. Comparing the results with previous research [26; 32; 33], it is stated that the models achieved higher recall values. Furthermore, a detailed breakdown of the recall values for individual classes is provided in Table 2 for YOLOv5n and Table 3 for YOLOv7. These tables likely contain a list of different classes and their corresponding recall values, allowing for a more fine-grained analysis of the model's performance across specific object categories. The recall metrics were calculated using the given equations:  
\[R=\frac{TP}{TP+FN}\]  
Figure 14: Precision metric graph of YOLOv5n. Figure 15: Precision metric graph of YOLOv7.