Salient object detection based on Drosophila vision-inspired model

The Drosophila visual system is extremely sensitive to moving targets, which provides a wealth of biological inspiration for the research of target motion perception in complex scenes, and also lays a biological theoretical foundation for the establishment of artificial drosophila visual neural networks. Drosophila's vision has been extensively studied in physiology, anatomy, and behavior, but our understanding of its underlying neural computing is still insufficient. In order to gain insight into the neural mechanism in Drosophila vision and take better advantage of its superiority in motion perception, we propose a Drosophila vision-inspired model, which constructs a complete Drosophila visual motion perception system by integrating continuous computing layers. Our hybrid model can fully demonstrate the motion perception process in Drosophila vision. In addition, the Drosophila vision-inspired model can also be exploited to salient object detection in dynamic scenes. This novel salient object detection model is different from the previous in that it can accurately identify the motion of interest (MOI) while suppressing background disturbances and ego-motion. Comprehensive evaluations using standard benchmarks demonstrate the superiority of our model in salient object detection compared with the state-of-the-art methods.