Towards human-leveled vision systems

The human visual system is a complex and interconnected network comprising billions of neurons. It plays an essential role in translating environmental light stimuli into information that guides and shapes human perception and action. Research on the visual system aims to uncover the underlying neural structure principles of human visual perception and their possible applications.Currently, there are two main approaches: biological system analysis and simulation, artificial intelligence models based on deep learning. Here we aim to discuss the two approaches to human-level vision systems. Deep learning has significantly impacted the field of vision with achievements in representation, modeling, and hardware design. However, there is still a significant gap between deep learning models and the human visual system in terms of scalability, transferability, and sustainability. The progress of the biological visual system can help fill the gap by further understanding the properties and functions of different components of the system. We take the efforts of reconstructing the retina as an example to illustrate that even if we are unable to replicate the visual system on a computer right now, we can still learn a lot by combining existing research outcomes in neuroscience. At the end of the paper, we suggest tracing back to gradually build visual systems from the computational counterpart of biological structures to achieve a human-level vision system in the future.

Snake-Hot-Eye-Assisted Multi-Process-Fusion Target Tracking Based on a Roll-Pitch Semi-strapdown Infrared Imaging Seeker

Aiming at intercepting large maneuvering targets precisely,the guidance law of advanced self-seeking missiles requires not only inertial line-of-sight(LOS)angular rate but also target maneuvering acceleration.Moreover,the semi-strapdown stabilization platform has lost the ability to measure the inertial LOS angular rate directly,which needs to be extracted by numerical calculation.The differential operation commonly used in traditional methods can magnify the measurement error of the sensor,resulting in insufficient calculation accuracy of the line-of-sight angular rate.By analyzing the mathematical relationship between the missile-target relative motion and the angle tracking system,a multi-process-fusion integrated filter model of relative motion and angle tracking is presented.To overcome the defect that the infrared seeker cannot directly measure the missile-target distance,following the snake-hot-eye visual mechanism,a visual bionic imaging guidance method of estimating the missile-target relative distance from the infrared images is proposed to improve the observability of the filter model.Finally,target-tracking simulations verify that the estimation accuracy of target acceleration is improved by four times.