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A Time-Delay Feedback Neural Network for Discriminating Small, Fast-Moving Targets in Complex Dynamic Environments

journal contribution
posted on 2024-01-04, 12:47 authored by H Wang, J Zhao, C Hu, J Peng, S Yue
Discriminating small moving objects within complex visual environments is a significant challenge for autonomous micro-robots that are generally limited in computational power. By exploiting their highly evolved visual systems, flying insects can effectively detect mates and track prey during rapid pursuits, even though the small targets equate to only a few pixels in their visual field. The high degree of sensitivity to small target movement is supported by a class of specialized neurons called small target motion detectors (STMDs). Existing STMD-based computational models normally comprise four sequentially arranged neural layers interconnected via feedforward loops to extract information on small target motion from raw visual inputs. However, feedback, another important regulatory circuit for motion perception, has not been investigated in the STMD pathway and its functional roles for small target motion detection are not clear. In this article, we propose an STMD-based neural network with feedback connection (feedback STMD), where the network output is temporally delayed, then fed back to the lower layers to mediate neural responses. We compare the properties of the model with and without the time-delay feedback loop and find that it shows a preference for high-velocity objects. Extensive experiments suggest that the feedback STMD achieves superior detection performance for fast-moving small targets, while significantly suppressing background false positive movements which display lower velocities. The proposed feedback model provides an effective solution in robotic visual systems for detecting fast-moving small targets that are always salient and potentially threatening.

Funding

10.13039/501100001809-National Natural Science Foundation of China (Grant Number: 12031003 and 11771347) European Union’s Horizon 2020 Research and Innovation Program through the Marie Sklodowska-Curie (Grant Number: 691154 STEP2DYNA and 778602 ULTRACEPT) 10.13039/501100002858-China Postdoctoral Science Foundation (Grant Number: 2019M662837)

History

Author affiliation

School of Computing and Mathematical Sciences, University of Leicester

Version

  • AM (Accepted Manuscript)

Published in

IEEE Transactions on Neural Networks and Learning Systems

Volume

34

Issue

1

Pagination

316 - 330

Publisher

IEEE

issn

2162-237X

eissn

2162-2388

Copyright date

2021

Available date

2024-01-04

Spatial coverage

United States

Language

eng

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