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Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

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Deploying TinyML for energy-efficient object detection and communication in low-power edge AI systems.

Ch Madhu Bhushan1, Priya Koppuravuri1, Nomitha Prasanthi1

  • 1Department of CSE, SRM University AP, Amaravati, Andhra Pradesh, India.

Scientific Reports
|December 5, 2025
PubMed
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This study optimizes Edge Artificial Intelligence (Edge AI) for microcontrollers by using model compression techniques like quantization. The resulting system offers efficient, real-time object detection for IoT devices with minimal resource usage.

Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Embedded Systems

Background:

  • Edge Artificial Intelligence (Edge AI) enables on-device processing for resource-constrained microcontrollers (MCUs).
  • Deploying deep learning models on MCUs faces challenges due to limited memory, computation, and energy.
  • Real-time, energy-efficient solutions are crucial for industrial automation, IoT, and smart devices.

Purpose of the Study:

  • To develop a real-time object detection system optimized for energy efficiency and scalability on MCUs.
  • To integrate model compression techniques, specifically quantization, with a lightweight neural network (MobileNetV2).
  • To analyze system-level trade-offs between latency, memory, energy consumption, and model size.

Main Methods:

  • Leveraged MobileNetV2, a lightweight neural network, and applied 8-bit post-training quantization.

Related Experiment Videos

Last Updated: Jan 9, 2026

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
03:31

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

996
  • Integrated a camera and Wi-Fi module with dual-mode TCP/UDP communication for data transmission.
  • Conducted system-level analysis using the Visual Wake Words (VWW) dataset on an MCU platform.

    • Main Results:

    • Achieved 3x storage reduction with 8-bit quantization, fitting within a 1MB flash/256KB SRAM budget (286-536 KB footprints).
    • On-device inference latency ranged from 3.47 to 14.98 ms per frame, with energy consumption of 10.6-22.1 J per inference.
    • Quantized MobileNet variants maintained accuracy, and UDP offered lower latency than TCP for wireless transmission.

    Conclusions:

    • The proposed system demonstrates a practical and scalable solution for real-time Edge AI on constrained hardware.
    • Integration of TinyML models with MCUs provides a foundation for autonomous, energy-efficient Edge AI.
    • Application-dependent protocol choices (TCP/UDP) are critical for balancing reliability and latency in embedded systems.