# Automated Detection and Classification of Returnable Packaging Based on YOLOV4 Algorithm
## 4 Conclusions  
This paper presents a detection algorithm for returnable packaging recycling. The method of creating a dataset, marking bounding boxes, and what to pay attention to while compiling the dataset is shown. In addition, the method of training the desired algorithm is shown, as settings that are used with its corresponding files, and the overall configuration is shown. Toward the end of the paper, the results are presented and the differences between each training cycle are explained. Quality results can be achieved through the use of AI. For example, by using CNN, it is possible to achieve high-quality detection algorithms. In this case, YOLOV4 gives almost perfect results. So the answers to the hypotheses defined in the introduction section are:  
* The YOLOV4 algorithm can achieve high classification accuracy in process of detection and classification of returnable packaging; however, the tuning process of YOLOV4 algorithm hyperparameters must be performed. * The classification accuracy was improved by tuning just two hyperparameters: max batch size, and type of activation function used in the YOLOV4 algorithm. The investigation showed that max batch size has a greater influence than the type of activation function. * The train/test dataset ratio did not have a notable influence on classification accuracy for a couple of reasons. The first reason is that the values of train/test ratios were the same as those ratios that are commonly used in other research papers. The lower size of the training dataset size would cause a weakly trained YOLOV4 model, while the higher size of the training dataset could cause potential overfitting of the YOLOV4 model. This paper shows different properties of the neural networks, in this case, it can be seen that by changing the hyperparameters such as max batch size, and activation function, different results can be achieved. The quality indicators of the trained algorithm were mAP, Average loss, and Average IoU, whereby YOLOV4 at 20,000 batches in train/test ratio of 75/25 with Mish activation function shows the flexibility of data processing and gives great results. The worst example of this work is the use of the Linear activation function which shows the worst results in all training cycles. ReLU and Mish show outstanding results,  
Figure 12: Results of YOLOV4 detection, for mixed packaging using web cam. but in this case, Mish prevails because of the computational properties, training speed, and data processing flexibility which has a greater application than the ReLU or Leaky ReLU activation function. These results are shown in Tables 4 and 5, in which in the last training cycle YOLOV4 achieves results of mAP 99.96%, F1 score 1.00 Average IoU 91.13%, and the Precision and Recall of 1.00, which is almost perfect considering the possible threat of the overfitting problem. The main contribution of this paper concerns the selection of different activation functions. The YOLOV4 detection algorithm uses the basic Mish activation function, which achieves superior properties however, the ReLU function has different computational properties than Mish. For this reason, it is not recommended to reject the ReLU activation function, because, for a different type of dataset, it is most likely that ReLU can surpass the results that are normally obtained using the default Mish activation function. Also, the importance of this paper is in devising innovative ideas that could accelerate the recycling process of recyclable materials/packaging. The problems of global climate change are visible in several aspects and future life must prevent natural disasters. Although the results are practically perfect, it is necessary to devote further research related to changing the YOLOV4 hyperparameters or YOLOV4 architecture to achieve even better and more accurate results. Conceptualization, Z.C. ; methodology, M.G., S.B.S. and N.A. ; software, M.G. ; validation, S.B.S. and N.A. ; formal analysis, N.A. and Z.C. ; investigation, M.G. and S.B.S. ; resources, S.B.S. and N.A. ; data curation, M.G.