Time-Delay Feedback Neural Network for Fast-Moving Small Target Discrimination Against Complex Dynamic Environments

摘要

Discriminating small moving objects in complex visual environments is a significant challenge for autonomous micro robots that are generally limited in computational power. Relying on well-evolved visual systems, flying insects can effortlessly detect mates and track prey in rapid pursuits, despite target sizes as small as a few pixels in the visual field. Such exquisite sensitivity for small target motion is known to be supported by a class of specialized neurons named as small target motion detectors (STMDs). The existing STMD-based models normally consist of four sequentially arranged neural layers interconnected through feedforward loops to extract motion information about small targets from raw visual inputs. However, feedback, another important regulatory circuit for motion perception, has not been investigated in the STMD pathway and its functional roles for small target motion detection are not clear. In this paper, we propose a STMD-based neural network with feedback connection (Feedback STMD), where the network output is temporally delayed, then fed back to lower layers to mediate neural responses. We compare the properties of the model with and without the time-delay feedback loop, and find it shows preference for high-velocity objects. Extensive experiments suggest that the Feedback STMD achieves superior detection performance for fast-moving small targets, while significantly suppresses background false positives with lower velocities. The proposed feedback model provides an effective solution for robotic vision systems to detect fast-moving small targets that are always salient and potentially threatening.