# Bio-Inspired Feedback Visual Network for Robust Small-Target Motion Detection in Complex Environments

## TL;DR

This paper introduces a bio-inspired AI model that improves small-target motion detection in complex environments by mimicking insect brain mechanisms.

## Contribution

A novel visual neural network with a feedback mechanism inspired by insect brains for robust small-target motion detection.

## Key findings

- The proposed model achieves a detection rate of 0.81 in complex visual scenarios.
- It outperforms compared models in Precision and F1-score by significant margins.
- A global inhibition module reduces false alarms from background motion cues.

## Abstract

In dynamic and complex real-world environments, artificial intelligence (AI) vision systems continue to face significant challenges in accurately detecting and tracking small objects. The core difficulty lies in the fact that small targets usually exhibit limited spatial and textural features, while dynamic backgrounds often generate numerous misleading motion cues, thereby interfering with reliable discrimination between targets and backgrounds. Inspired by the remarkable capability of the insect brain in detecting small moving objects, this study proposes a visual neural network model enhanced by a feedback mechanism. By adaptively responding to temporal variations, the proposed model is able to more precisely distinguish small targets from background-induced false targets. The network architecture consists of two main pathways: a motion detection pathway that extracts motion-related features from minute targets, and a feedback attention pathway that enhances the focus on true targets by leveraging the feature differences between real and false motion signals. In addition, a global inhibition module is incorporated to reduce the false alarm rate by filtering out background-induced false positives, thereby improving the overall detection performance of the model. Experimental results demonstrate that the proposed model achieves a detection rate of 0.81 in complex visual scenarios, whereas the compared models all achieve detection rates below 0.59, indicating a significant improvement in detection performance. Meanwhile, in terms of Precision and F1-score, the proposed model achieves values of 0.0648 and 0.12, respectively, while the compared models obtain values lower than 0.0077 and 0.015, further validating the superiority of the proposed method in detection accuracy and robustness.