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ORIGINAL RESEARCH article

Front. Comput. Neurosci.
Volume 19 - 2025 | doi: 10.3389/fncom.2025.1452203
This article is part of the Research Topic Computational Intelligence for Signal and Image Processing, Volume II View all 9 articles

Motion Feature Extraction Using Magnocellular-inspired Spiking Neural Networks for Drone Detection

Provisionally accepted
Jiayi Zheng Jiayi Zheng 1,2Yaping Wan Yaping Wan 1Xin Yang Xin Yang 2Hua Zhong Hua Zhong 1Minghua Du Minghua Du 3Gang Wang Gang Wang 2,4*
  • 1 University of South China, Hengyang, China
  • 2 Center of Brain Sciences, Beijing Institute of Basic Medical Sciences, Beijing, China
  • 3 Department of Emergency, The First Medical Center, Chinese PLA General Hospital, Beijing, China
  • 4 Chinese Institute for Brain Research, Beijing (CIBR), Beijing, Beijing Municipality, China

The final, formatted version of the article will be published soon.

    Traditional object detection methods usually underperform when locating tiny or small drones against complex backgrounds, since the appearance features of the targets and the backgrounds are highly similar. To address this, inspired by the magnocellular motion processing mechanisms, we proposed to utilize the spatial-temporal characteristics of the flying drones based on spiking neural networks, thereby developing the Magno-Spiking Neural Network (MG-SNN) for drone detection. The MG-SNN can learn to identify potential regions of moving targets through motion saliency estimation and subsequently integrates the information into the popular object detection algorithms to design the retinal-inspired spiking neural network module for drone motion extraction and object detection architecture, which integrates motion and spatial features before object detection to enhance detection accuracy. To design and train the MG-SNN, we propose a new backpropagation method called Dynamic Threshold Multi-frame Spike Time Sequence (DT-MSTS), and establish a dataset for the training and validation of MG-SNN, effectively extracting and updating visual motion features. Experimental results in terms of drone detection performance indicate that the incorporation of MG-SNN significantly improves the accuracy of low-altitude drone detection tasks compared to popular small object detection algorithms, acting as a cheap plug-and-play module in detecting small flying targets against complex backgrounds.

    Keywords: bio-inspired vision computation1, spiking neural networks2, motion detection3, drone target recognition4, motion saliency estimation5, visual motion features6

    Received: 20 Jun 2024; Accepted: 06 Jan 2025.

    Copyright: © 2025 Zheng, Wan, Yang, Zhong, Du and Wang. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence: Gang Wang, Center of Brain Sciences, Beijing Institute of Basic Medical Sciences, Beijing, China

    Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.