TSOM: Small Object Motion Detection Neural Network Inspired by Avian
  Visual Circuit
Pignge Hu Xiaoteng Zhang Mengmeng Li Yingjie Zhu Li Shi
Detecting small moving objects in complex backgrounds from an overhead
perspective is a highly challenging task for machine vision systems. As an
inspiration from nature, the avian visual system is capable of processing
motion information in various complex aerial scenes, and its Retina-OT-Rt
visual circuit is highly sensitive to capturing the motion information of small
objects from high altitudes. However, more needs to be done on small object
motion detection algorithms based on the avian visual system. In this paper, we
conducted mathematical modeling based on extensive studies of the biological
mechanisms of the Retina-OT-Rt visual circuit. Based on this, we proposed a
novel tectum small object motion detection neural network (TSOM). The neural
network includes the retina, SGC dendritic, SGC Soma, and Rt layers, each layer
corresponding to neurons in the visual pathway. The Retina layer is responsible
for accurately projecting input content, the SGC dendritic layer perceives and
encodes spatial-temporal information, the SGC Soma layer computes complex
motion information and extracts small objects, and the Rt layer integrates and
decodes motion information from multiple directions to determine the position
of small objects. Extensive experiments on pigeon neurophysiological
experiments and image sequence data showed that the TSOM is biologically
interpretable and effective in extracting reliable small object motion features
from complex high-altitude backgrounds.