# Image Classification of Parcel Boxes under the Underground Logistics System Using CNN MobileNet
## 2 Related Research  
In this study, we intended to conduct an experiment to classify damaged parcels based on the checkpoints at places where the recognition and classification of parcels should be detected in, as shown in Table 1, under ULS. Research to detect and classify an object's quality using a convolutional neural network is being applied in various fields. Research on the classification of parcel boxes is relatively new, and there are no published studies to directly compare our results to. A large amount of research has been conducted on the quality of fruit, road damage, waste classification, and disease. [PERSON], [PERSON] et al. performed an experiment to distinguish defects in lemons using VGG16, and classified healthy and unhealthy lemons with an accuracy of 88.75% [5]. [PERSON], [PERSON] et al. performed a study to classify seven skin diseases using the MobileNet algorithm, with an accuracy of 94.4% [6]. [PERSON], [PERSON] et al. conducted a study using Inception V2 and MobileNet to classify road damage into eight types using road surface images taken while driving a vehicle, and showed an accuracy of 77% [7]. [PERSON], [PERSON] et al. performed an experiment to classify damaged ceilings in large-span structures using CNN. This study aimed to overcome the limitations of operator-based on-site inspection. As a result, classifying two types of damaged ceilings had an accuracy of 86.22% [8]. [PERSON], [PERSON] et al. proposed a waste image classification using DenseNet 169 to improve the efficiency and accuracy of waste sorting and treatment by conducting a study to classify waste into five categories, with an accuracy of 82.8% [9]. [PERSON], [PERSON] et al. conducted a study to detect and classify COVID-19 using MobileNet using chest X-ray images, which had an accuracy of 96.33% [10]. [PERSON] et al. conducted an experiment to classify the freshness of fish by detecting fish eyes and applying bottleneck and expansion to MobileNet, finding an accuracy of 99.36% [11]. \begin{table}
\begin{tabular}{p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt}} \hline \hline  & \multicolumn{2}{c}{**Railway Freight Yard**} & \multicolumn{2}{c}{**Subway Station**} & \multicolumn{2}{c}{**Ground Site for Preparation Delivery**} & \multicolumn{1}{p{56.9 pt}}{**Last-Mile**} \\ \cline{2-7}
**Receiving** & \multicolumn{1}{p{56.9 pt}}{**Sorting**} & \multicolumn{1}{p{56.9 pt}}{**Originating Station**} & \multicolumn{1}{p{56.9 pt}}{**Destination Station**} & \multicolumn{1}{p{56.9 pt}}{**Moving**} & \multicolumn{1}{p{56.9 pt}}{**Receiving**} & \multicolumn{1}{p{56.9 pt}}{**Sorting**} & \multicolumn{1}{p{56.9 pt}}{**Fight Car**} \\ \hline Required detection location & \(\surd\) & \(\surd\) & & \(\surd\) & \(\surd\) & \(\surd\) & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Required detection location of parcels. Figure 2: Process of underground logistics system. Table 2 shows that the existing image classification research was mainly conducted with the binary classification of objects, and the image classification model was different according to the purpose and utilization of image classification. In this study, an experiment was conducted to classify normal and damaged parcel boxes using MobileNet. MobileNet was chosen as it uses a relatively small amount of disk space by using a small number of computations and parameters compared to other models. Therefore, in order to be applied to ULS in the future, the MobileNet model, which is lightweight, efficient and quick, was selected as the basic model [12]. The remainder of this paper is structured as follows: Section 1 investigates the research background of classifying damaged parcel boxes using a convolutional neural network (CNN) under an underground logistics system (ULS). As a result of analyzing literature reviews, there were no published works related to the classification of parcel boxes using CNN that we could directly compare to this research.