## 4 Proposed Scheme
### Product Identification System Based on YOLO v4 Stacking and Height Occlusion  
For the deep learning model, the target object must appear completely in the image before the product can be identified. As long as the product is slightly obscured, the product may be identified incorrectly. Simple object identification cannot solve the problem of stacked products due to occlusion. We designed a set of methods that can solve the problems of stacked goods and height occlusion, which can effectively improve the utilization of space and can also greatly reduce the construction cost of unmanned stores.  
First of all, we designed an identification model for stacking products and height occlusion; this model has six goods categories, but is extended into eight categories because of two added categories, \"Top of Bottle\" and \"Top of Can\", which are used to solve the occlusion problem mentioned later, such as bottled Coca-Cola. Aluminum can tops, Pringles tops, bottled beverage tops, etc. are all products of this type. In this way, once the product itself is blocked, the number of top covers can still be used to calculate the number of products.  
There are three calculation methods for commodity appearance, as shown in Table 2. The first calculation method can be directly identified through the YOLO model. As the training sample contains many occlusions and stacking situations, after a large amount of data enhancement and training, a model that can identify more than half of the occluded products is formed, as shown in Figure 10.  
The second calculation method is because some products have long and narrow shapes, only the top can be identified when they are densely arranged. Therefore, the calculation method is to calculate the number of recognized top caps, such as bottled beverages and canned snacks. In this way, there are several top covers which are equivalent to the quantity of several commodities, as shown in Figure 10.  
The third calculation method requires two cameras to identify stacked products, which are iron cans and aluminum foil packages, as shown in Figure 10. First, the top of the product is regarded as a category, referred to as \"top covers\", using two cameras. From top views, the number of products is calculated from the top angle, but it is impossible to determine whether the products are stacked. The side view image can be used to judge whether the product is on the first layer or stacked on the second layer. The number of \"top covers\" is discovered by looking down on the top-view camera, and then using the side camera to adjust the camera angle to find the stacked products.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Method** & **Commodity Category** \\ \hline Directly identify the product and & Noodles, packaged snacks, and boxed snacks, as shown in Figure 10 \\ calculate the quantity & in Figure 10 \\ \hline Calculate the top & Bottled drinks and Pringles, as shown in Figure 10 \\ \hline Dual camera calculation & Iron, aluminum cans, and aluminum foil packages, as shown in Figure 10 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Identification methods used for height occlusion and stacking.