# Parcel Classification and Positioning of Intelligent Parcel Storage System Based on YOLOv5
## 5 Conclusions and Future Work  
In this paper, based on YOLOv5, a parcel box recognition study was conducted to build an intelligent parcel storage system. The YOLOv5 model, which is divided into four detailed versions according to the performance and size of the model, was used for comparative analysis of parcel box recognition, and 3410 images were collected for the experiment. As a result of the experiment, it was analyzed that the precision, recall, and F1 of the YOLOv5l model were 0.966, 0.899, and 0.932, respectively, showing a stronger performance than the other models. The YOLOv5l model showed a performance 2.6% higher in precision, 0.6% higher in recall, and 1.6% higher in F1 score compared with the other version. In the case of the parameter value indicating the size of the model, the YOLOv5l model showed a larger value than YOLOv5s, m but showed a value half that of the YOLOv5x model. As a result, YOLOv5l showed the best performance in terms of the size of the model, and it was analyzed as an object recognition model suitable for application to an actual delivery box. In this study, a parcel recognition model with optimal performance was proposed through an experiment using the YOLOv5 model, and a basis for applying object recognition technology to an actual intelligent parcel storage system was laid.  
In this study, a parcel recognition experiment was conducted to build an intelligent parcel storage system using the YOLOv5 model, and the following research limitations and future research tasks were derived. Firstly, it is necessary to conduct experiments in more diverse environments and more diverse datasets by securing additional datasets. Secondly, with the rapid development of the YOLO model, the YOLOv7 model appeared in July 2022. Therefore, it is necessary to conduct parcel recognition research using the latest model. In addition, through model transformation such as pruning, it is possible to propose an improved model which is optimized for parcel recognition as a focus of future study. Finally, by applying the object recognition model to the parcel storage system under development, future research will be able to confirm its performance in the real environment.  
Conceptualization, Y.K.; methodology, M.K.; software, M.K.; resources, M.K. and Y.K.; data curation, writing--original draft preparation, M.K.; writing--review and editing, Y.K.; supervision, Y.K.; project administration, Y.K.; funding acquisition, Y.K. All authors have read and agreed to the published version of the manuscript.  
This work was supported by a grant from R&D program of the Korea Evaluation Institute of Industrial Technology (20014664).  
Not applicable.  
The data presented in this study are available on request from the corresponding author. The data are not publicly available due to personal information in the invoice of the parcels.  
Figure 7: Test result of YOLOv5l.  
**Acknowledgments:** The authors would like to thank the participants who volunteered to participate in this experiment.  
**Conflicts of Interest:** The authors declare no conflict of interest.