## 3 Dataset Preparation and Experimental Setup  
In order to detect and recognize objects during cleaning operations, robot vacuum cleaners rely on integrated front-facing cameras to capture ground-view perspective videos. Due to the unique ground-view perspective and uncommon object categories (for example, soybean, cat/dog food, and millet), I could not find any appropriate public dataset that fits our needs in this research. None of the existing datasets in refs. [14; 15; 16; 17; 18; 19; 20; 21; 22; 23] contain categories of indoor small suctionable objects, so they were unusable in this work. Therefore, I had to create and prepare a dataset. Note that it might be possible to use a non-ground-view dataset for ground-view object detection, but its performance depends on the similarity between the two domains and the availability of ground-view data for fine-tuning. If the domains differ significantly, the inference performance of AI models may be limited, and additional adaptation techniques may be required. It is not an easy task to create a dataset for AI model training, since it involves collecting and organizing a representative set of data samples. The quality of a dataset plays a crucial role in the inference performance and generalization capability of trained AI models. The accuracy of trained AI models is directly proportional to the quality of training data. Training data contain input data and annotations. AI models learn the annotations on the training data, so that they may be applied to new, unlabeled data samples for inference. In this study, I gathered and compiled video clips from a variety of robot vacuum cleaners in different, diverse buildings. Since raw data can be contaminated and damaged in various ways, I pre-processed the collected video data to ensure a uniform image format for AI model training. When generating a dataset, data augmentation techniques are employed to artificially expand its diversity and size. The complexity of an AI model is directly influenced by the input image size [25; 26]. As the dimensions of input images grow larger, the number of model parameters also increases to accommodate the augmented information. This is necessary because larger images often contain more intricate details that demand a more intricate model for accurate capture and processing. Conversely, reducing the size of input images can simplify the model by decreasing the number of parameters and computations needed. However, this reduction comes at the cost of potential information loss and diminished accuracy, as finer details might be overlooked. The original size of each raw image is 1280 \(\times\) 720, which indicates high-resolution images that contain more details or information. However, higher resolutions demand more computational resources, such as processing power and memory [9]. Therefore, I decided to scale down the dimensions of the raw images to 256 \(\times\) 144, while maintaining the aspect ratio. In this way, AI models can be trained with less training data more quickly, leading to faster convergence and a shorter training time. In this work, a total of 23,042 raw images were divided into three subsets, with each subset containing 50%, 25%, and 25% of the original images. The number of samples for the training set, validation set, and testing set are 15,855, 3339, and 3391, respectively. The training set was used to train AI models, the validation set helped in tuning hyper-parameters and preventing over-fitting, and the test set was used to evaluate the final inference performance. This research involved selecting eight categories of common recyclable indoor small objects in each subset. Since robot vacuum cleaners are not well equipped to handle larger or heavier objects, these categories of suctionable objects in the dataset had to be small items. As shown in Table 1, the chosen categories included red bean, millet, soybean, mung bean, sunflower seed shell, cat/dog food, rice, and green bean.