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Modeling bio-inspired visual neural for detecting visual features of small- and wide-field moving targets synchronously from complex dynamic environments

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Abstract

The synchronous detection of visual features of small- and wide-field moving targets in complex dynamic environments has been a challenge in the field of moving target detection. Fortunately, the visual system of Drosophila flies can detect visual features of small- and wide-field moving targets synchronously from complex dynamic environments, thus providing a good paradigm for the synchronous detection of visual features of small- and wide-field moving targets in complex dynamic environments, however, there is little literature that comprehensively analyses and verify this. In this paper, we present a bio-inspired computing model for detecting visual features of small- and wide-field moving targets synchronously. The model consists of three stages. First, visual stimuli are perceived and divided into parallel ON and OFF pathways. Then, the feedback mechanism and the full Hassenstein-Reichardt correlator are applied to the Medulla neurons. Finally, the Lobula Columnar 11 is used to detect visual features of small-field moving targets, i.e., the position, meanwhile, the Lobula Plate Tangential Cell is utilized to detect visual features of wide-field moving targets, i.e., the translational directional selectivity. Through extensive experiments, the proposed model can detect visual features of small- and wide-field moving targets synchronously. In addition, the proposed model improves the detection rate in small-field moving target detection by 17.18% compared with the traditional bio-inspired computing model, while the effectiveness of the proposed model is further verified by comparing it with the conventional moving target detection methods. Moreover, the proposed model can also effectively detect visual features of wide-field moving targets. The source code can be found at https://github.com/szhanghh/A-bio-inspired-visual-neural-computing-model.

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Data availability

The datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request.

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Acknowledgements

This research was funded by the Natural Science Foundation of Jiangxi Province under grant No. 20232BAB202003.

Funding

Funding Natural Science Foundation of Jiangxi Province, 20232BAB202003.

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Authors and Affiliations

  1. College of Information Science and Engineering, Hohai University, Changzhou, 213200, China

    Sheng Zhang & Jingjing Tang

  2. School of Mechanical and Electrical Engineering, Nanchang Institute of Technology, Nanchang, 330044, China

    Ke Li & Dan Zhou

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Contributions

Sheng Zhang: Conceptualization, Methodology, Writing-Original Draft. Ke Li: Software, Funding acquisition, Validation, Formal analysis. Dan Zhou: Software, Resources, Visualization, Project Administration. Jingjing Tang: Data curation, Writing- Reviewing and Editing, Supervision, Investigation.

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Correspondence to Sheng Zhang or Ke Li.

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Zhang, S., Li, K., Zhou, D. et al. Modeling bio-inspired visual neural for detecting visual features of small- and wide-field moving targets synchronously from complex dynamic environments. SIViP 18, 8881–8898 (2024). https://doi.org/10.1007/s11760-024-03515-4

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