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Robot vacuum cleaners have gained widespread popularity as household appliances. One significant challenge in enhancing their functionality is to identify and classify small indoor objects suitable for safe suctioning and recycling during cleaning operations. However, the current state of research faces several difficulties, including the lack of a comprehensive dataset, size variation, limited visual features, occlusion and clutter, varying lighting conditions, the need for real-time processing, and edge computing. In this paper, I address these challenges by investigating a lightweight AI model specifically tailored for robot vacuum cleaners. First, I assembled a diverse dataset containing 23,042 ground-view perspective images captured by robot vacuum cleaners. Then, I examined state-of-the-art AI models from the existing literature and carefully selected three high-performance models (Xception, DenseNet121, and MobileNet) as potential model candidates. Subsequently, I simplified these three selected models to reduce their computational complexity and overall size. To further compress the model size, I employed post-training weight quantization on these simplified models. In this way, our proposed lightweight AI model strikes a balance between object classification accuracy and computational complexity, enabling real-time processing on resource-constrained robot vacuum cleaner platforms. I thoroughly evaluated the performance of the proposed AI model on a diverse dataset, demonstrating its feasibility and practical applicability. The experimental results show that, with a small memory size budget of 0.7 MB, the best AI model is L-w Xception 1, with a width factor of 0.25, whose resultant object classification accuracy is 84.37%. When compared with the most accurate state-of-the-art model in the literature, this proposed model accomplished a remarkable memory size reduction of 350 times, while incurring only a slight decrease in classification accuracy, i.e., approximately 4.54%. [ABSTRACT FROM AUTHOR]

Artificial intelligence (AI) plays a critical role in the operation of robot vacuum cleaners, enabling them to intelligently navigate to clean and avoid indoor obstacles. Due to limited computational resources, manufacturers must balance performance and cost. This necessitates the development of lightweight AI models that can achieve high performance. Traditional uniform weight quantization assigns the same number of levels to all weights, regardless of their distribution or importance. Consequently, this lack of adaptability may lead to sub-optimal quantization results, as the quantization levels do not align with the statistical properties of the weights. To address this challenge, in this work, we propose a new technique called low bitwidth strong non-uniform quantization, which largely reduces the memory footprint of AI models while maintaining high accuracy. Our proposed non-uniform quantization method, as opposed to traditional uniform quantization, aims to align with the actual weight distribution of well-trained neural network models. The proposed quantization scheme builds upon the observation of weight distribution characteristics in AI models and aims to leverage this knowledge to enhance the efficiency of neural network implementations. Additionally, we adjust the input image size to reduce the computational and memory demands of AI models. The goal is to identify an appropriate image size and its corresponding AI models that can be used in resource-constrained robot vacuum cleaners while still achieving acceptable accuracy on the object classification task. Experimental results indicate that when compared to the state-of-the-art AI models in the literature, the proposed AI model achieves a 2-fold decrease in memory usage from 15.51 MB down to 7.68 MB while maintaining the same accuracy of around 93%. In addition, the proposed non-uniform quantization model reduces memory usage by 20 times (from 15.51 MB down to 0.78 MB) with a slight accuracy drop of 3.11% (the classification accuracy is still above 90%). Thus, our proposed high-performance and lightweight AI model strikes an excellent balance between model complexity, classification accuracy, and computational resources for robot vacuum cleaners. [ABSTRACT FROM AUTHOR]

Although optimizing deep neural networks is becoming crucial for deploying the networks on edge AI devices, it faces increasing challenges due to scarce hardware resources in modern IoT and mobile devices. This study proposes a quantization method that can quantize all internal computations and parameters in the memory modification. Unlike most previous methods that primarily focused on relatively simple CNN models for image classification, the proposed method, Unified Scaling-Based Pure-Integer Quantization (USPIQ), can handle more complex CNN models for object detection. USPIQ aims to provide a systematic approach to convert all floating-point operations to pure-integer operations in every model layer. It can significantly reduce the computational overhead and make it more suitable for low-power neural network accelerator hardware consisting of pure-integer datapaths and small memory aimed at low-power consumption and small chip size. The proposed method optimally calibrates the scale parameters for each layer using a subset of unlabeled representative images. Furthermore, we introduce a notion of the Unified Scale Factor (USF), which combines the conventional two-step scaling processes (quantization and dequantization) into a single process for each layer. As a result, it improves the inference speed and the accuracy of the resulting quantized model. Our experiment on YOLOv5 models demonstrates that USPIQ can significantly reduce the on-chip memory for parameters and activation data by ~75% and 43.68%, respectively, compared with the floating-point model. These reductions have been achieved with a minimal loss in mAP@0.5—at most 0.61%. In addition, our proposed USPIQ exhibits a significant improvement in the inference speed compared to ONNX Run-Time quantization, achieving a speedup of 1.64 to 2.84 times. We also demonstrate that USPIQ outperforms the previous methods in terms of accuracy and hardware reduction for 8-bit quantization of all YOLOv5 versions. [ABSTRACT FROM AUTHOR]

With the rapid development of artificial intelligence (AI) theory, particularly deep learning neural networks, robot vacuums equipped with AI power can automatically clean indoor floors by using intelligent programming and vacuuming services. To date, several deep AI models have been proposed to distinguish indoor objects between cleanable litter and noncleanable hazardous obstacles. Unfortunately, these existing deep AI models focus entirely on the accuracy enhancement of object classification, and little effort has been made to minimize the memory size and implementation cost of AI models. As a result, these existing deep AI models require far more memory space than a typical robot vacuum can provide. To address this shortcoming, this paper aims to study and find an efficient deep AI model that can achieve a good balance between classification accuracy and memory usage (i.e., implementation cost). In this work, we propose a weight-quantized SqueezeNet model for robot vacuums. This model can classify indoor cleanable litters from noncleanable hazardous obstacles based on the image or video captures from robot vacuums. Furthermore, we collect videos or pictures captured by built-in cameras of robot vacuums and use them to construct a diverse dataset. The dataset contains 20,000 images with a ground-view perspective of dining rooms, kitchens and living rooms for various houses under different lighting conditions. Experimental results show that the proposed deep AI model can achieve comparable object classification accuracy of around 93% while reducing memory usage by at least 22.5 times. More importantly, the memory footprint required by our AI model is only 0.8 MB, indicating that this model can run smoothly on resource-constrained robot vacuums, where low-end processors or microcontrollers are dedicated to running AI algorithms. [ABSTRACT FROM AUTHOR]