## 4 Classification of Domain Knowledge  
### Model Selection and Usage Strategies  
The service robot platform is constrained by computing power, which limits the model's parameter size and inference time. As a result, using networks with a large depth for direct training would lead to long training periods, and parameters cannot be updated promptly during the training process. Additionally, the large model size makes it difficult to deploy the model onto the service robot platform, resulting in delays in making timely predictions. Furthermore, the robot requires a high level of responsiveness for real-time services. MobileNetV3 is specifically designed to optimize speed and efficiency. By using depthwise separable convolutions, MobileNetV3 significantly reduces the number of parameters and model size. Moreover, MobileNetV3 is optimized for deployment on mobile and edge devices. Its optimized architecture ensures that it can run efficiently on these edge devices, enabling the robot to perform fast local inference while minimizing latency and energy consumption, thus guaranteeing the model's quick response capabilities. Therefore, in this study, MobileNetV3 is used as the convolutional module of the model to ensure the efficient extraction of high-dimensional features from image data. As shown in Figure 6, the network structure of MobileNetV3 is divided into three parts. The difference is the number of basic unit bencks and internal parameters, mainly the number of channels. MobileNetV3 includes two versions (large and small) that are suitable for different scenarios. The overall structure of the two versions is the same, but the difference is the number of basic units bottlenecks (bneck) and the internal parameters, such as the number of channels. Table 1 lists the parameters of MobileNetV3 (large) used in this work. Since people live in indoor environments, few datasets are available due to privacy concerns. In this study, we used the CIFAR-100 and ImageNet datasets. Therefore, it is difficult to train deep learning models that require many parameters. In addition, a large model with many parameters deployed on the mobile terminal may be limited by the hardware computing power, and real-time applications may not be possible. Moreover, indoor images have worse lighting conditions and more occlusions than outdoor images,  
Figure 5: The acquisition mechanism of missing attributes category, physical, and visual attributes of objects. Figure 6: Network structure of MobileNetV3. and it is difficult to perform effective feature extraction to describe the rich semantic features in the image. Transfer learning strategies compensate for the shortcomings of convolutional neural networks for these tasks. Transfer learning is a technique that leverages pre-trained models to extract useful features from large and diverse datasets, followed by fine-tuning the model to adapt to a new dataset specific to the current task. As shown in Figure 7, the transfer learning strategy applied in this study enables the model, through pre-training on a large dataset, to learn visual features such as edges, textures, and patterns from various types of images. These features are shared across different domains, while also acquiring weights. After completing model pre-training, the linear classifier of the pre-trained model is removed, and the remaining network layers are used as convolutional layers with their weights frozen. This means that the weights of these layers will not be updated during further training. These frozen convolutional layers retain the general visual features learned from the large dataset. Finally, the new linear layer is used as the classifier, and only the parameter gradients of the last classification layer are updated on the indoor scene dataset, substantially improving the classification accuracy of the model. The parameters of the convolutional layer obtained from pre-training contain the characteristic information of the original field.