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An Edge-Intelligent Flotation Monitoring and Control System based on 8051 MCU and Offline EfficientNet-B5 Inference on Raspberry Pi

Boyu Yang, Fangyuan Yao, Wenqi Bai, Yuxin Zhang, Hang Zhu, Zhaoning Yin
International Core Journal of Engineering, (2025), Vol.11, No.6, pp.426-437
Published: May 28, 2025
DOI: 10.6919/ICJE.202506_11(6).0046
PDF: Download Full Text PDF
Abstract

To address the limitations of traditional flotation processes such as delayed feedback and low detection precision, this paper proposes an edge-intelligent flotation monitoring and control system. The system integrates a low-power 8051 microcontroller unit (MCU) for real-time multimodal sensor data acquisition with a Raspberry Pi for local image-based state recognition. An EfficientNet-B5 model, pre-trained and quantized via TensorFlow Lite, is deployed offline on the Raspberry Pi to enable high-accuracy flotation froth classification with an average inference latency of 250–350 ms. Through attention-based feature refinement and float32-quantized model optimization, the proposed system achieves a 67.5% reduction in memory usage and an 18.4% decrease in average inference time compared to the original model. In addition, the mean and peak power consumption are reduced by 18.3% and 9.5%, respectively, ensuring stable performance in low-power embedded environments. A Tkinter-based GUI further supports both manual and automatic control modes, enabling intelligent adjustments in flotation operations. The proposed architecture realizes a closed-loop control chain from sensing to decision-making and actuation, offering a scalable and energy-efficient solution for intelligent mineral processing at the edge.

Keywords: Edge Computing; 8051 MCU; Raspberry Pi; EfficientNet-B5; Flotation Monitoring; Intelligent Control; Embedded System; Offline Inference.
APA Citation: Boyu Yang, Fangyuan Yao, Wenqi Bai, Yuxin Zhang, Hang Zhu, Zhaoning Yin (2025). An Edge-Intelligent Flotation Monitoring and Control System based on 8051 MCU and Offline EfficientNet-B5 Inference on Raspberry Pi. International Core Journal of Engineering, 11(6), 426-437. https://doi.org/10.6919/ICJE.202506_11(6).0046

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