A bio-inspired model for object motion direction and speed detection against colored backgrounds

Building upon the verified neural mechanisms of the biological visual system, this paper introduces a bio-inspired model for object motion state detection. The model aims to ensure high detection accuracy while addressing the interpretability issues prevalent in current deep learning models. The proposed Object Motion Detection System (OMDS) draws inspiration from the directional and speed sensitivity observed in the biological visual system, which should inherently arise from physiological structures rather than learned behaviors. Therefore, it is feasible to replicate motion detection functionalities through bio-inspired modeling by simulating the structure of the visual system. To validate this concept, we conducted extensive experiments to assess the detection accuracy and robustness of OMDS under various conditions. Additionally, we compared its performance with convolutional neural networks ResNet and ResNeXt, under identical conditions. The results demonstrate that in the given dataset, OMDS not only surpasses the performance of convolutional neural network models but also reflects characteristics observed in the biological visual system, which results in high accuracy, low hardware requirements, and enhanced interpretability.

• Publication year

  2024

• Venue

  International Conference on Computer Vision and Information Technology

• Publication date

  2024-12-02

• Fields of study

  Biology, Computer Science, Engineering

• Identifiers
  DOI 10.1117/12.3056633
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• No claims are published for this paper.

• No concepts are published for this paper.

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