## 1 Introduction
This issue is pervasive across all robotic vacuum models available on the current market that incorporate IR sensor technology. The presence of dark coloration on the surface results in a reduction in their capacity to capture visual features. Occlusion and Clutter: In indoor environments, small objects can be partially or fully occluded by other objects or hidden within cluttered backgrounds. This occlusion and clutter poses challenges for object recognition algorithms, as they may struggle to detect and classify small objects accurately when they are not fully visible. Real-Time Processing: Real-time processing demands quick and efficient execution, which is challenging on resource-constrained devices like robotic vacuum cleaners that have limited a memory footprint, power budget, and computational resources. Sophisticated models that achieve high accuracy in small object classification tend to be computationally expensive, and hence, power hungry. Striking a balance between model complexity and accuracy is crucial to meet real-time processing demands and the energy budget for running AI on these edge devices. Size Variation: Small suctionable objects can vary significantly in size, shape, and texture. For example, cat food pellets or kibble come in various sizes and shapes, from small and round to larger, irregular pieces. This diversity makes it hard to develop an effective classification model that accurately identifies all types of small suctionable objects. The variation in appearance and characteristics requires robust AI algorithms capable of handling different object attributes. Varying Lighting Conditions: Adequate lighting is essential for the visibility of small objects. In good lighting conditions, small objects are more easily distinguishable from the background, and their features are more prominent for better contrast. However, under poor lighting conditions (for example, harsh shadows), it is challenging for AI models to discern relevant features. On the other hand, strong sources of light, such as direct sunlight or artificial light, can cause highlighting and glare on small objects. These reflections may overwhelm the object's features, leading to wrong predictions from AI models. The seven research challenges mentioned above also serve as the limitations of the study, which constitute the central focus of this paper. The main contributions and innovations of this paper are described as follows: First, I gathered videos or pictures taken by the built-in cameras of robot vacuum cleaners and utilized them to create a diverse dataset for small suctionable indoor object classification. The dataset encompasses 23,042 ground-view perspective images captured from dining rooms, kitchens, and living rooms in various houses and at different lighting conditions. This approach ensures data diversity in complex indoor scenes. Moreover, the dataset contains ample image data (at least 2000 images in each category of rice, cat/dog food, sunflower seed shell, red bean, soybean, green been, cat litter, and millet) with eight balanced classes, making it suitable for effective training and classification purposes. Second, I examined state-of-the-art AI models including Xception, Inception-V3, LeNet-5, AlexNet, VGG-16, ResNet, DenseNet, EfficientNet, ShuffleNet, NasNetMobile, and MobileNet and carefully chose the top three models among them as potential candidates. Then, I simplified these three potential models and implemented weight quantization to minimize memory usage and computational complexity, ensuring their deployability on resource-constrained robot vacuum cleaner systems. By implementing these model simplification and memory reduction techniques, the memory sizes for both potential model candidates were substantially decreased. The initial memory size of the state-of-the-art AI models can be compressed to only 0.7 MB in our proposed model, comfortably fitting within the memory budget limitations of robot vacuum cleaners.