## 4 Conclusions and Future Work  
This work presents a computer vision system along with its integration into an IIoT platform for identifying and classifying the fill levels of waste containers used for collecting metal scrap in manufacturing industries. Deep Learning models and more specifically the VGG16 CNN model has been trained for performing the classification task in a case study deriving from a copper tube plant. The industrial case study demonstrates that the VGG16 model has sufficient accuracy for classifying the fill levels of metal scrap containers which allows their practical application in industrial environments. In the context of the industrial pilot case, two VGG16 CNN models have been trained for two different metal scrap containers. The models have been trained with the batch size parameter set to 32, with approximately 9.000 annotated images for one model and approximately 11.000 images for the second model. They achieved an accuracy ranging from 77.5% to 95%, depending on the model and on the testing dataset. Moreover, the work also presents an implemented IIoT-based system to manage the information generated by the computer vision system and communicate it to the industrial users via the IIoT information stack. The proposed method can be implemented using standard, relatively low-cost, off-the-shelf hardware and software components for computer vision systems, especially for cases in which other sensor-based systems cannot be applied. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Precision** & **Recall** & **F1-Score** & **Weighted Average** \\ \hline
0.7743 & 0.7745 & 0.7744 & 0.8307 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Evaluation indexes of customized VGG16 Bin 2 model. \begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline
**Delta Of Misclassification** & **1** & **2** & **3** & **4** & **5** & **6** & **7** & **8** & **9** & **10** & **11** & **12** & **TOTAL** \\ \hline Sum of Misclassified Images & 11 & 16 & 14 & 4 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 45 \\ \% Relatively to the Entire & 24.5\% & 35.5\% & 31.1\% & 8.9\% & 0\% & 0\% & 0\% & 0\% & 0\% & 0\% & 0\% & 100\% \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Analysis of the delta difference between the calculated fill level and the actual one for the customized VGG16 model in Bin 2. The current work did not investigate the performance of the model based on different types of waste in the bins. However, it can be expected that the trained model would be a good starting point that can be further retrained with additional images that would, however, be considerably less in volume compared to the ones used in the current work, thus saving costs from model development task. One shortcoming of the proposed method is that two distinct models were trained for each of the two bins of the industrial pilot case. Due to the intrinsic differences in the camera field of view between bin 1 and bin 2, depicted in the images (see Figure 7), the pictures between the two bins differ significantly. In order to have achieve a common, generalized model, mounting of a second camera would be required, which would provide a similar field of view to bin 2, similar to the existing field of view for bin 1, which, however, would require additional costs that would not be accepted by the factory management, given the fact that bin 2 does not have the same impact on the operation of the work center as bin 1. However, investigating the generalization of the CNN model will be a topic of future research. The images were labelled by experts who visually estimated the fill level. However, due to the geometry of the copper tube waste, their estimates could be biased, as the visual difference in 10% increments could be small, and as a result, similar images might be labelled to different classes. The issue of mislabelled images can be responsible for some of the model misclassifications.