## 2 Related Work
The newly constructed trash dataset contains more than 8100 images encompassing seven distinct categories of objects. The authors showed that the most effective model is EfficientNetB3, which achieved the highest test accuracy of 92.87%. In ref. [21], the authors used the Inception-V3 model for classifying office garbage (such as can, bottle, milk-box, paper cup, paper, and battery). The Inception-V3 model, which was developed by Google, builds on the innovative concept of using multiple filter sizes in parallel to capture information at various scales within the input image. This enables the Inception-V3 model to achieve a remarkable accuracy on object classification while efficiently handling the trade-off between depth and computational resources. The experimental results show the test accuracy to be 95.33% while the number of model parameters is around 21.8 million. In ref. [22], the authors used simplified Xception models for garbage image classification. By removing part of the network layers, lightweight Xception models are developed with a smaller number of training parameters. This makes it well suited to tasks that require a high efficiency, such as real-time or resource-constrained applications, while still benefiting from the architectural advancements of the Xception family. As a result, the reported object classification accuracy reached 94.34%. From the above-mentioned discussion, it is evident that these existing research works [14; 15; 16; 17; 18; 19; 20; 21; 22] do not fully meet the demand for lightweight AI models catering to the classification of small suctionable objects in resource-limited robot vacuum cleaner systems. To date, two primary obstacles have impeded the progress in this area. The first obstacle refers to the lack of a comprehensive and diverse dataset, specifically focusing on ground-view videos and images collected from robot vacuum cleaners. For training a robust AI model for small suctionable object classification, it is crucial to have a large and varied dataset that encompasses different environmental conditions, lighting variations, object sizes, and cluttered scenes typically encountered by robot vacuum cleaners during their operation. However, such a dataset tailored for indoor cleaning scenarios involving robot vacuum cleaners is not readily available. To overcome this obstacle, researchers need to devote considerable efforts to collect and create such a dataset. It is important to note that the study [23] offers a dataset comprising 14 categories, but these categories, includingbed, sofa, cabinet, stool, table, close stool, trashcan, slippers, wire, socks, carpet, book, feces, and curtain, do not encompass small suctionable objects. As a result, the dataset provided in ref. [23] is not suitable for small object classification and recycling purposes. Another obstacle lies in the fact that, even though some of these models are considered lightweight, their memory requirements still exceed the limitations of low-cost CPUs or micro-controllers. For instance, micro-controllers like the STM32F7 series typically offer a memory capacity of only 512 KB or 1 MB [24], which is insufficient to accommodate these AI models. Hence, the critical objective of this paper was to devise lightweight AI models tailored for small suctionable object classification and recycling, ensuring both a high test accuracy and minimal memory footprint.