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Related Experiment Video

Updated: Oct 20, 2025

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

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Published on: December 15, 2023

A Lightweight Pedestrian Detection Engine with Two-Stage Low-Complexity Detection Network and Adaptive Region

Luying Que¹, Teng Zhang¹, Hongtao Guo¹

¹School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China.

Sensors (Basel, Switzerland)

|September 10, 2021

Summary

This study introduces a lightweight pedestrian detection engine for mobile applications. The new engine significantly reduces computational load and power consumption while maintaining high accuracy, outperforming existing models.

Keywords:

FPGA adaptive lightweight neural network pedestrian detection

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Area of Science:

Computer Vision
Artificial Intelligence
Embedded Systems

Background:

Deep learning models for pedestrian detection offer high accuracy but suffer from significant computational complexity.
This complexity limits their deployment on hardware- and power-constrained mobile platforms like surveillance drones.
Existing methods often fail to balance detection performance with computational efficiency for edge devices.

Purpose of the Study:

To develop a lightweight pedestrian detection engine suitable for mobile and embedded applications.
To reduce computational complexity and power consumption without sacrificing detection accuracy.
To enable efficient pedestrian detection on resource-limited platforms such as drones.

Main Methods:

Proposed a two-stage low-complexity detection network combined with an adaptive region focusing technique.
Developed a lightweight pedestrian detection engine with significantly reduced parameters (0.73 M) and operations (1.04 B).
Implemented and compared the proposed engine with YOLOv3 and YOLOv3-Tiny on a Xilinx FPGA Zynq7020.

Main Results:

Achieved comparable precision (85.18%) and miss rate (25.16%) to existing pedestrian detection designs.
The proposed engine demonstrated superior performance on the FPGA, reaching 16.3 Frames Per Second (Fps) at 0.59 Watts (W).
Outperformed YOLOv3 (5.3 Fps, 2.43 W) and YOLOv3-Tiny (12.8 Fps, 0.95 W) in terms of speed and power efficiency.

Conclusions:

The proposed lightweight pedestrian detection engine effectively addresses the computational challenges of deep learning models on mobile devices.
It offers a viable solution for real-time pedestrian detection in power- and hardware-constrained applications like drone surveillance.
The engine provides a significant advancement in balancing performance and efficiency for edge AI applications.