## 2 Related Work  
To the best of our knowledge, there has been no prior research on indoor small suctionable object classification for robot vacuum cleaners, and the majority of the existing literature focuses on outdoor trash or garbage classification [14; 15; 16; 17; 18; 19; 20]. In this section, I conduct a review of the state-of-the-art AI models employed for outdoor garbage classification. By studying these works, I gained valuable insights that aided in designing specialized AI models for indoor suctionable small object classification, specifically tailored for robot vacuum cleaners. The authors of [15] introduce a novel robot system with the primary objective of automating the process of collecting litter and garbage from grassy areas. This innovative system leverages the power of deep learning techniques to enable the robot to identify and classify various types of objects commonly found as litter in outdoor environments. The specific object categories targeted by the system include bottles, cans, cartons, plastic bags, and wasted paper, etc. Throughout the research and experimentation, the ResNet34 model demonstrated remarkable performance, achieving an accuracy rate of up to 96% in real-world scenarios. The model of ResNet34 showed impressive capabilities in learning complex features from images, making it promising for the challenging task of small suctionable object classification in indoor environments. In ref. [16], the researchers introduced an intelligent waste material classification system designed to streamline the process. This system was developed using the ResNet50 convolutional neural network model to simplify and optimize the waste classification procedure. The system serves the purpose of categorizing waste into distinct groups, including glass, metal, paper, plastic, and more. During testing on the trash image dataset, the proposed system attained an impressive accuracy of 87%. In ref. [17], the researchers introduced a novel approach for waste object detection in unstructured environments using Mask R-CNN based on a backbone ResNet101 architecture. The proposed method effectively categorizes waste materials such as plastic, paper, glass, and metal. The experimental findings revealed that the reported accuracy of the Mask R-CNN model is 97%. In ref. [18], using a dataset consisting of 2527 trash images, the MobileNet model was trained to classify various common trashes, including glass, paper, cardboard, plastic, metal, and more. In addition, the authors used the Gzip quantization algorithm to compress the model weights of trained MobileNet models. Gzip is a widely used data compression method that reduces the size of the model by encoding the weights in a more compact form. Unlike 8-bit weight quantization, the Gzip algorithm does not have a direct impact on the precision of the model's parameters. The authors of [18] reported the test accuracy of the MobileNet model to be 87.2%. In ref. [14], the authors used three popular AI models (MobileNetV2, NasNetMobile, and EfficientNetB0) for edge computing towards waste object classification.The dataset consists of 10 waste categories, encompassing items like food bowls, food boxes, glass bottles, metal cans, plastic bags, plastic bottles, snack wraps, plastic cutlery, and tetrapacks. In comparison to previous studies mentioned in refs. [15; 16; 17; 18], the three selected models achieved a comparable test accuracy while consuming less memory. In ref. [19], the authors proposed using an enhanced version of the ShuffleNetV2 architecture for garbage classification. The authors explain how these enhancements lead to an improved accuracy and computational efficiency, making the model well suited for garbage classification applications. The experimental results show the test accuracy to be about 98% while the number of model parameters is only 1.3 million. In ref. [20], the authors developed a unique dataset and conducted a comparative analysis of various deep learning methods for waste classification.