# Parcel Classification and Positioning of Intelligent Parcel Storage System Based on YOLOv5
## 2 Related Work  
### Literature Review  
As personal and non-face-to-face life became commonplace after COVID-19, the courier logistics industry is also activating untact and e-commerce delivery. At the same time, AI (Artificial Intelligence) technology began to be applied to various fields including the manufacturing field, thus the logistics industry, which relied on manpower, is changing. The development of unmanned parcel storage that connects delivery drivers and consumers in the final stage of the delivery process, and the application of AI technology are increasing. However, while the speed at which AI technology is being applied to the existing unmanned delivery system is fast, there is a lack of research on the application of AI technology to the unmanned parcel storage system. Instead, research on parcel recognition, not parcel storage, has mainly been conducted to identify parcels through technologies such as GPS and RFID in the transportation and storage process of parcels [5; 6]. This technology helps to track the delivery status and route of parcels, but many related studies have already been conducted since 2013, thus this research is now common, limiting further research because it is already widely used in the logistics industry.  
Although few studies apply AI technology to parcel recognition, research is gradually increasing. In the study of [PERSON] and [PERSON], an image recognition experiment that can be applied to the logistics industry was conducted usingAI's deep learning technology. In the study, image classification was performed using EfficientNet, and the learning accuracy achieved was 83% [7]. [PERSON] et al. conducted a study applying AI technology to classify parcels within the urban railway system. As a performance method, experiments were conducted by comparing several deep learning models such as MobileNet, ResNet50, and VGG16 [8]. The research conducted by [PERSON] et al. was conducted before this research, and was conducted to classify both normal and defective parcel boxes, and the experiment focused on object classification rather than object recognition and location estimation. A study was also conducted to distinguish between breakage and non-damage in parcel recognition. An image recognition study was conducted to increase the recognition rate of parcels and speed by transforming the existing AI deep learning model. As a result of the study, the recognition accuracy of the delivery box was 86.75% and proved itself to be effective [9].  
As shown in Table 1, looking at the studies conducted so far, GPS and RFID-related studies have been mainly conducted in the past, and now, based on these studies, most of them are applied to and operated in actual systems. Presently, studies on the recognition and classification of delivery boxes are mainly conducted, but the recognition rate remains at 80%, which performance is not particularly high.  
Therefore, in this study, we propose an unmanned parcel system and conduct a study on an algorithm that can improve parcel recognition performance by applying suitable AI technology. Unlike the existing literature, this study intends to use object recognition technology to build a robot loading system for unmanned delivery systems in the future, rather than deep learning classification technology for box recognition.