A Target Imaging and Recognition Method Based on Raptor Vision

Abstract

It is a big challenge to quickly and accurately recognize targets in a complex background. The mutual constraints between a wide field of vision (FOV) and high resolution affect the optical tracking and imaging ability in a wide area. In nature, raptors possess unique imaging structures and optic nerve systems that can accurately recognize prey. This paper proposes an imaging system combined with a deep learning algorithm based on the visual characteristics of raptors, aiming to achieve wide FOV, high spatial resolution, and accurate recognition ability. As for the imaging system, two sub-optical systems with different focal lengths and various-size photoreceptor cells jointly simulate the deep fovea of a raptor’s eye. The one simulating the peripheral region has a wide FOV and high sensitivity for capturing the target quickly by means of short focal length and large-size photoreceptor cells, and the other imitating the central region has high resolution for recognizing the target accurately through the long focal length and small-size photoreceptor cells. Furthermore, the proposed algorithm with an attention and feedback network based on octave convolution (AOCNet) simulates the mechanism of the optic nerve pathway by adding it into the convolutional neural network (CNN), thereby enhancing the ability of feature extraction and target recognition. Experimental results show that the target imaging and recognition system eliminates the limitation between wide FOV and high spatial resolution, and effectively improves the accuracy of target recognition in complex backgrounds.

Keywords

• target recognition
• high-resolution imaging
• wide FOV
• biological-based imaging and processing
• AOCNet convolutional neural network