## 2 The Automatic Plates Recognition System
### Clean Plate Recognition Method  
The process of plate recognition in the target imagery involves locating and identifying the status of the plates (clean or not clean). The system utilizes a deep neural network based on semantic segmentation for plate recognition, which essentially follows a two-step process: (1) Inputting the training dataset into the network, which iteratively derives a fitting function; (2) Feeding the test dataset into the network, where the fitting function computes the recognition results. Selecting a suitable semantic segmentation network from the many available is crucial. Considering both hardware and software capabilities and practical needs, we employed three mature networks: U-Net, PSPNet, and DeepLab V3+ networks. In this study, we first compare the performance of the three networks in the task of recognizing CD-ROMs through quantitative analysis, and we select the network with the best performance for qualitative comparison. The main indexes for used quantitative comparison are the convergence speed and recognition accuracy of the networks, followed by qualitative analysis to further demonstrate the recognition effect of each model in different environments and tableware states. In the qualitative analysis, two typical scenarios, simple and complex, are selected for evaluation: the simple scenario includes a single empty plate arrangement, while the complex scenario involves different types of dinner plates with food residue. Due to the limited number of plate image scenes actually acquired, the selection of experimental scenes is relatively small, but it covers the typical situations commonly found in the cafeteria environment, which can effectively reflect the generalization ability of the model. The structure and characteristics of the three networks are as follows:  
U-Net is a symmetric network with a simple structure that is capable of recognizing multi-scale features [14]. Its structure is shown in Figure 4.  
The network structure is divided into three parts: downsampling, upsampling, and skip connections. During the encoding process, the network gradually reduces the image size through convolution and downsampling while extracting low-level features, such as the shape of the tray and the boundaries of food residues. During the decoding process, the network gradually restores the image size through upsampling and convolution, capturing high-level semantic features such as the specific location and shape of the leftover food. The skip connections in the middle fuse the features from the encoding and decoding stages, effectively preserving spatial information and improving the accuracy of tray details and food waste recognition. Through this structure, U-Net can efficiently extract key features necessary for clean plate recognition, enhancing the accuracy and robustness of object detection. PSPNet has also been applied to the clean plate recognition task, with one of its key features being the use of auxiliary loss functions to accelerate the network's convergence speed [15]. This network structure can integrate image features from four different scales, as shown in Figure 5. Each sub-region generates feature maps of different sizes through average pooling, which are then upsampled and merged. In this way, PSPNet enhances the understanding of global contextual information, which is particularly beneficial for accurately capturing global patterns of food residues and the overall layout of trays in clean plate recognition, reducing the likelihood of mis-segmentation. Additionally, the multi-scale feature fusion in PSPNet improves the model's robustness when handling complex scenarios, especially in tray recognition tasks, allowing it to better handle trays and food residues of varying sizes and shapes. DeepLab V3+: With its encoder-decoder structure and edge segmentation enhancement features, DeepLab V3+ addresses the issue of feature loss caused by excessive sampling.