arXiv subjects

Qinbing Fu

Publications and source records attributed to Qinbing Fu.

At least 19 recordsLinked to original sources

arXiv2026

A Bio-Plausible Visual Neural Network for Locust-Inspired Collision Perception

Locust visual systems have long served as an important biological paradigm for studying looming perception and collision avoidance. Numerous computational models have successfully reproduced the selective responses of Lobula Giant Movement Detector (LGMD) neurons to approaching objects, thereby emulating the fundamental functionality of the biological system. However, existing models remain limited in biological plausibility and robustness when operating in complex and dynamic visual environments. To address these limitations, we propose a biologically plausible neural network for locust-inspired looming detection. The proposed framework incorporates a spatially isotropic sampling strategy that mimics the ommatidial organization of the locust compound eye, a population-voting mechanism inspired by population coding in biological neural systems, and leaky integrate-and-fire neuronal dynamics to replace conventional sigmoid-based membrane activation. Systematic experiments on synthetic stimuli, laboratory sequences, and real-world driving scenarios demonstrate that the proposed model improves robustness under challenging visual conditions while preserving computational efficiency and enhancing biological fidelity. These results highlight the potential of biologically grounded neural computation for robust and efficient collision perception.

cs.NE↗

arXiv2026

An Event-Driven Framework for Fly-Inspired Visual Motion Detection

Fast and reliable motion detection is essential for machine vision and autonomous systems operating in dynamic environments. This work integrates emerging event-based sensing with biologically structured neural computation to establish an efficient computational paradigm for visual motion detection. The proposed framework is built upon a recently developed fly-inspired neural network that emulates motion-processing circuits in the optic lobe. Owing to its feed-forward and training-free architecture, the neural model requires only a small number of interpretable parameters and is well suited for real-time implementation. Event cameras provide low-latency, low-power, and high-dynamic-range visual sensing by asynchronously transmitting brightness-change events. However, their performance can be degraded by event noise, including temporal noise and junction-leakage-induced activity, particularly under low-light conditions. Moreover, effective integration between event-based visual representations and biologically inspired neural processing remains under-explored. To address these challenges, we propose an event-driven computational framework that combines time-surface encoding for front-end event representation with a fly optic-lobe-inspired neural network for foreground motion-direction estimation. A bottom-up attention mechanism is further incorporated to suppress background motion and enhance the saliency of foreground targets. The proposed method is evaluated via real-world datasets of ground-vehicle detection and compared with a baseline frame-based model and an optimization-based approach. Experimental results demonstrate that the framework effectively combines the temporal advantages of event-driven vision with the efficiency and interpretability of bio-inspired neural processing.

cs.CV↗

arXiv2025

Emergence of robust looming selectivity via coordinated inhibitory neural computations

In the locust's lobula giant movement detector neural pathways, four categories of inhibition, i.e., global inhibition, self-inhibition, lateral inhibition, and feed-forward inhibition, have been functionally explored in the context of looming perception. However, their combined influence on shaping selectivity to looming motion remains unclear. Driven by recent physiological advancements, this paper offers new insights into the roles of these inhibitory mechanisms at multiple levels and scales in simulations, refining the specific selectivity for responding only to objects approaching the eyes while remaining unresponsive to other forms of movement. Within a feed-forward, multi-layer neural network framework, global inhibition, lateral inhibition, self-inhibition, and feed-forward inhibition are integrated. Global inhibition acts as an immediate feedback mechanism, normalising light intensities delivered by ommatidia, particularly addressing low-contrast looming. Self-inhibition, modelled numerically for the first time, suppresses translational motion. Lateral inhibition is formed by delayed local excitation spreading across a larger area. Notably, self-inhibition and lateral inhibition are sequential in time and are combined through feed-forward inhibition, which indicates the angular size subtended by moving objects. Together, these inhibitory processes attenuate motion-induced excitation at multiple levels and scales. This research suggests that self-inhibition may act earlier than lateral inhibition to rapidly reduce excitation in situ, thereby suppressing translational motion, and global inhibition can modulate excitation on a finer scale, enhancing selectivity in higher contrast range.

q-bio.NC↗

arXiv2025

How Fly Neural Perception Mechanisms Enhance Visuomotor Control of Micro Robots

Anyone who has tried to swat a fly has likely been frustrated by its remarkable agility.This ability stems from its visual neural perception system, particularly the collision-selective neurons within its small brain.For autonomous robots operating in complex and unfamiliar environments, achieving similar agility is highly desirable but often constrained by the trade-off between computational cost and performance.In this context, insect-inspired intelligence offers a parsimonious route to low-power, computationally efficient frameworks.In this paper, we propose an attention-driven visuomotor control strategy inspired by a specific class of fly visual projection neurons-the lobula plate/lobula column type-2 (LPLC2)-and their associated escape behaviors.To our knowledge, this represents the first embodiment of an LPLC2 neural model in the embedded vision of a physical mobile robot, enabling collision perception and reactive evasion.The model was simplified and optimized at 70KB in memory to suit the computational constraints of a vision-based micro robot, the Colias, while preserving key neural perception mechanisms.We further incorporated multi-attention mechanisms to emulate the distributed nature of LPLC2 responses, allowing the robot to detect and react to approaching targets both rapidly and selectively.We systematically evaluated the proposed method against a state-of-the-art locust-inspired collision detection model.Results showed that the fly-inspired visuomotor model achieved comparable robustness, at success rate of 96.1% in collision detection while producing more adaptive and elegant evasive maneuvers.Beyond demonstrating an effective collision-avoidance strategy, this work highlights the potential of fly-inspired neural models for advancing research into collective behaviors in insect intelligence.

cs.RO↗

arXiv2025

Attention-Driven LPLC2 Neural Ensemble Model for Multi-Target Looming Detection and Localization

Lobula plate/lobula columnar, type 2 (LPLC2) visual projection neurons in the fly's visual system possess highly looming-selective properties, making them ideal for developing artificial collision detection systems. The four dendritic branches of individual LPLC2 neurons, each tuned to specific directional motion, enhance the robustness of looming detection by utilizing radial motion opponency. Existing models of LPLC2 neurons either concentrate on individual cells to detect centroid-focused expansion or utilize population-voting strategies to obtain global collision information. However, their potential for addressing multi-target collision scenarios remains largely untapped. In this study, we propose a numerical model for LPLC2 populations, leveraging a bottom-up attention mechanism driven by motion-sensitive neural pathways to generate attention fields (AFs). This integration of AFs with highly nonlinear LPLC2 responses enables precise and continuous detection of multiple looming objects emanating from any region of the visual field. We began by conducting comparative experiments to evaluate the proposed model against two related models, highlighting its unique characteristics. Next, we tested its ability to detect multiple targets in dynamic natural scenarios. Finally, we validated the model using real-world video data collected by aerial robots. Experimental results demonstrate that the proposed model excels in detecting, distinguishing, and tracking multiple looming targets with remarkable speed and accuracy. This advanced ability to detect and localize looming objects, especially in complex and dynamic environments, holds great promise for overcoming collision-detection challenges in mobile intelligent machines.

cs.NE↗

arXiv2025

Dynamic Neural Field Modeling of Visual Contrast for Perceiving Incoherent Looming

Amari's Dynamic Neural Field (DNF) framework provides a brain-inspired approach to modeling the average activation of neuronal groups. Leveraging a single field, DNF has become a promising foundation for low-energy looming perception module in robotic applications. However, the previous DNF methods face significant challenges in detecting incoherent or inconsistent looming features--conditions commonly encountered in real-world scenarios, such as collision detection in rainy weather. Insights from the visual systems of fruit flies and locusts reveal encoding ON/OFF visual contrast plays a critical role in enhancing looming selectivity. Additionally, lateral excitation mechanism potentially refines the responses of loom-sensitive neurons to both coherent and incoherent stimuli. Together, these offer valuable guidance for improving looming perception models. Building on these biological evidence, we extend the previous single-field DNF framework by incorporating the modeling of ON/OFF visual contrast, each governed by a dedicated DNF. Lateral excitation within each ON/OFF-contrast field is formulated using a normalized Gaussian kernel, and their outputs are integrated in the Summation field to generate collision alerts. Experimental evaluations show that the proposed model effectively addresses incoherent looming detection challenges and significantly outperforms state-of-the-art locust-inspired models. It demonstrates robust performance across diverse stimuli, including synthetic rain effects, underscoring its potential for reliable looming perception in complex, noisy environments with inconsistent visual cues.

cs.NE↗

arXiv2025

A Bio-Inspired Research Paradigm of Collision Perception Neurons Enabling Neuro-Robotic Integration: The LGMD Case

Compared to human vision, locust visual systems excel at rapid and precise collision detection, despite relying on only hundreds of thousands of neurons organized through a few neuropils. This efficiency makes them an attractive model system for developing artificial collision-detecting systems. Specifically, researchers have identified collision-selective neurons in the locust's optic lobe, called lobula giant movement detectors (LGMDs), which respond specifically to approaching objects. Research upon LGMD neurons began in the early 1970s. Initially, due to their large size, these neurons were identified as motion detectors, but their role as looming detectors was recognized over time. Since then, progress in neuroscience, computational modeling of LGMD's visual neural circuits, and LGMD-based robotics have advanced in tandem, each field supporting and driving the others. Today, with a deeper understanding of LGMD neurons, LGMD-based models have significantly improved collision-free navigation in mobile robots including ground and aerial robots. This review highlights recent developments in LGMD research from the perspectives of neuroscience, computational modeling, and robotics. It emphasizes a biologically plausible research paradigm, where insights from neuroscience inform real-world applications, which would in turn validate and advance neuroscience. With strong support from extensive research and growing application demand, this paradigm has reached a mature stage and demonstrates versatility across different areas of neuroscience research, thereby enhancing our understanding of the interconnections between neuroscience, computational modeling, and robotics. Furthermore, this paradigm would shed light upon the modeling and robotic research into other motion-sensitive neurons or neural circuits.

cs.NE↗

arXiv2021

Profiling Visual Dynamic Complexity Using a Bio-Robotic Approach

Visual dynamic complexity is a ubiquitous, hidden attribute of the visual world that every dynamic vision system is faced with. However, it is implicit and intractable which has never been quantitatively described due to the difficulty in defending temporal features correlated to spatial image complexity. To fill this vacancy, we propose a novel bio-robotic approach to profile visual dynamic complexity which can be used as a new metric. Here we apply a state-of-the-art brain-inspired motion detection neural network model to explicitly profile such complexity associated with spatial-temporal frequency (SF-TF) of visual scene. This model is for the first time implemented in an autonomous micro-mobile robot which navigates freely in an arena with visual walls displaying moving sine-wave grating or cluttered natural scene. The neural dynamic response can make reasonable prediction on surrounding complexity since it can be mapped monotonically to varying SF-TF of visual scene. The experiments show this approach is flexible to different visual scenes for profiling the dynamic complexity. We also use this metric as a predictor to investigate the boundary of another collision detection visual system performing in changing environment with increasing dynamic complexity. This research demonstrates a new paradigm of using biologically plausible visual processing scheme to estimate dynamic complexity of visual scene from both spatial and temporal perspectives, which could be beneficial to predicting input complexity when evaluating dynamic vision systems.

cs.RO↗

arXiv2020

Modelling Drosophila Motion Vision Pathways for Decoding the Direction of Translating Objects Against Cluttered Moving Backgrounds

Decoding the direction of translating objects in front of cluttered moving backgrounds, accurately and efficiently, is still a challenging problem. In nature, lightweight and low-powered flying insects apply motion vision to detect a moving target in highly variable environments during flight, which are excellent paradigms to learn motion perception strategies. This paper investigates the fruit fly \textit{Drosophila} motion vision pathways and presents computational modelling based on cutting-edge physiological researches. The proposed visual system model features bio-plausible ON and OFF pathways, wide-field horizontal-sensitive (HS) and vertical-sensitive (VS) systems. The main contributions of this research are on two aspects: 1) the proposed model articulates the forming of both direction-selective (DS) and direction-opponent (DO) responses, revealed as principal features of motion perception neural circuits, in a feed-forward manner; 2) it also shows robust direction selectivity to translating objects in front of cluttered moving backgrounds, via the modelling of spatiotemporal dynamics including combination of motion pre-filtering mechanisms and ensembles of local correlators inside both the ON and OFF pathways, which works effectively to suppress irrelevant background motion or distractors, and to improve the dynamic response. Accordingly, the direction of translating objects is decoded as global responses of both the HS and VS systems with positive or negative output indicating preferred-direction (PD) or null-direction (ND) translation. The experiments have verified the effectiveness of the proposed neural system model, and demonstrated its responsive preference to faster-moving, higher-contrast and larger-size targets embedded in cluttered moving backgrounds.

q-bio.NC↗

arXiv2020

Complementary Visual Neuronal Systems Model for Collision Sensing

Inspired by insects' visual brains, this paper presents original modelling of a complementary visual neuronal systems model for real-time and robust collision sensing. Two categories of wide-field motion sensitive neurons, i.e., the lobula giant movement detectors (LGMDs) in locusts and the lobula plate tangential cells (LPTCs) in flies, have been studied, intensively. The LGMDs have specific selectivity to approaching objects in depth that threaten collision; whilst the LPTCs are only sensitive to translating objects in horizontal and vertical directions. Though each has been modelled and applied in various visual scenes including robot scenarios, little has been done on investigating their complementary functionality and selectivity when functioning together. To fill this vacancy, we introduce a hybrid model combining two LGMDs (LGMD-1 and LGMD-2) with horizontally (rightward and leftward) sensitive LPTCs (LPTC-R and LPTC-L) specialising in fast collision perception. With coordination and competition between different activated neurons, the proximity feature by frontal approaching stimuli can be largely sharpened up by suppressing translating and receding motions. The proposed method has been implemented in ground micro-mobile robots as embedded systems. The multi-robot experiments have demonstrated the effectiveness and robustness of the proposed model for frontal collision sensing, which outperforms previous single-type neuron computation methods against translating interference.

cs.RO↗

arXiv2019

Synthetic Neural Vision System Design for Motion Pattern Recognition in Dynamic Robot Scenes

Insects have tiny brains but complicated visual systems for motion perception. A handful of insect visual neurons have been computationally modeled and successfully applied for robotics. How different neurons collaborate on motion perception, is an open question to date. In this paper, we propose a novel embedded vision system in autonomous micro-robots, to recognize motion patterns in dynamic robot scenes. Here, the basic motion patterns are categorized into movements of looming (proximity), recession, translation, and other irrelevant ones. The presented system is a synthetic neural network, which comprises two complementary sub-systems with four spiking neurons -- the lobula giant movement detectors (LGMD1 and LGMD2) in locusts for sensing looming and recession, and the direction selective neurons (DSN-R and DSN-L) in flies for translational motion extraction. Images are transformed to spikes via spatiotemporal computations towards a switch function and decision making mechanisms, in order to invoke proper robot behaviors amongst collision avoidance, tracking and wandering, in dynamic robot scenes. Our robot experiments demonstrated two main contributions: (1) This neural vision system is effective to recognize the basic motion patterns corresponding to timely and proper robot behaviors in dynamic scenes. (2) The arena tests with multi-robots demonstrated the effectiveness in recognizing more abundant motion features for collision detection, which is a great improvement compared with former studies.

cs.NE↗

arXiv2019

An LGMD Based Competitive Collision Avoidance Strategy for UAV

Building a reliable and efficient collision avoidance system for unmanned aerial vehicles (UAVs) is still a challenging problem. This research takes inspiration from locusts, which can fly in dense swarms for hundreds of miles without collision. In the locust's brain, a visual pathway of LGMD-DCMD (lobula giant movement detector and descending contra-lateral motion detector) has been identified as collision perception system guiding fast collision avoidance for locusts, which is ideal for designing artificial vision systems. However, there is very few works investigating its potential in real-world UAV applications. In this paper, we present an LGMD based competitive collision avoidance method for UAV indoor navigation. Compared to previous works, we divided the UAV's field of view into four subfields each handled by an LGMD neuron. Therefore, four individual competitive LGMDs (C-LGMD) compete for guiding the directional collision avoidance of UAV. With more degrees of freedom compared to ground robots and vehicles, the UAV can escape from collision along four cardinal directions (e.g. the object approaching from the left-side triggers a rightward shifting of the UAV). Our proposed method has been validated by both simulations and real-time quadcopter arena experiments.

cs.RO↗

arXiv2019

Constant Angular Velocity Regulation for Visually Guided Terrain Following

Insects use visual cues to control their flight behaviours. By estimating the angular velocity of the visual stimuli and regulating it to a constant value, honeybees can perform a terrain following task which keeps the certain height above the undulated ground. For mimicking this behaviour in a bio-plausible computation structure, this paper presents a new angular velocity decoding model based on the honeybee's behavioural experiments. The model consists of three parts, the texture estimation layer for spatial information extraction, the motion detection layer for temporal information extraction and the decoding layer combining information from pervious layers to estimate the angular velocity. Compared to previous methods on this field, the proposed model produces responses largely independent of the spatial frequency and contrast in grating experiments. The angular velocity based control scheme is proposed to implement the model into a bee simulated by the game engine Unity. The perfect terrain following above patterned ground and successfully flying over irregular textured terrain show its potential for micro unmanned aerial vehicles' terrain following.

cs.RO↗

arXiv2019

Towards Computational Models and Applications of Insect Visual Systems for Motion Perception: A Review

Motion perception is a critical capability determining a variety of aspects of insects' life, including avoiding predators, foraging and so forth. A good number of motion detectors have been identified in the insects' visual pathways. Computational modelling of these motion detectors has not only been providing effective solutions to artificial intelligence, but also benefiting the understanding of complicated biological visual systems. These biological mechanisms through millions of years of evolutionary development will have formed solid modules for constructing dynamic vision systems for future intelligent machines. This article reviews the computational motion perception models originating from biological research of insects' visual systems in the literature. These motion perception models or neural networks comprise the looming sensitive neuronal models of lobula giant movement detectors (LGMDs) in locusts, the translation sensitive neural systems of direction selective neurons (DSNs) in fruit flies, bees and locusts, as well as the small target motion detectors (STMDs) in dragonflies and hover flies. We also review the applications of these models to robots and vehicles. Through these modelling studies, we summarise the methodologies that generate different direction and size selectivity in motion perception. At last, we discuss about multiple systems integration and hardware realisation of these bio-inspired motion perception models.

cs.CV↗

arXiv2019

A Visual Neural Network for Robust Collision Perception in Vehicle Driving Scenarios

This research addresses the challenging problem of visual collision detection in very complex and dynamic real physical scenes, specifically, the vehicle driving scenarios. This research takes inspiration from a large-field looming sensitive neuron, i.e., the lobula giant movement detector (LGMD) in the locust's visual pathways, which represents high spike frequency to rapid approaching objects. Building upon our previous models, in this paper we propose a novel inhibition mechanism that is capable of adapting to different levels of background complexity. This adaptive mechanism works effectively to mediate the local inhibition strength and tune the temporal latency of local excitation reaching the LGMD neuron. As a result, the proposed model is effective to extract colliding cues from complex dynamic visual scenes. We tested the proposed method using a range of stimuli including simulated movements in grating backgrounds and shifting of a natural panoramic scene, as well as vehicle crash video sequences. The experimental results demonstrate the proposed method is feasible for fast collision perception in real-world situations with potential applications in future autonomous vehicles.

cs.CV↗

arXiv2019

Angular Velocity Estimation of Image Motion Mimicking the Honeybee Tunnel Centring Behaviour

Insects use visual information to estimate angular velocity of retinal image motion, which determines a variety of flight behaviours including speed regulation, tunnel centring and visual navigation. For angular velocity estimation, honeybees show large spatial-independence against visual stimuli, whereas the previous models have not fulfilled such an ability. To address this issue, we propose a bio-plausible model for estimating the image motion velocity based on behavioural experiments of the honeybee flying through patterned tunnels. The proposed model contains mainly three parts, the texture estimation layer for spatial information extraction, the delay-and-correlate layer for temporal information extraction and the decoding layer for angular velocity estimation. This model produces responses that are largely independent of the spatial frequency in grating experiments. And the model has been implemented in a virtual bee for tunnel centring simulations. The results coincide with both electro-physiological neuron spike and behavioural path recordings, which indicates our proposed method provides a better explanation of the honeybee's image motion detection mechanism guiding the tunnel centring behaviour.

q-bio.NC↗

arXiv2019

Visual Cue Integration for Small Target Motion Detection in Natural Cluttered Backgrounds

The robust detection of small targets against cluttered background is important for future artificial visual systems in searching and tracking applications. The insects' visual systems have demonstrated excellent ability to avoid predators, find prey or identify conspecifics - which always appear as small dim speckles in the visual field. Build a computational model of the insects' visual pathways could provide effective solutions to detect small moving targets. Although a few visual system models have been proposed, they only make use of small-field visual features for motion detection and their detection results often contain a number of false positives. To address this issue, we develop a new visual system model for small target motion detection against cluttered moving backgrounds. Compared to the existing models, the small-field and wide-field visual features are separately extracted by two motion-sensitive neurons to detect small target motion and background motion. These two types of motion information are further integrated to filter out false positives. Extensive experiments showed that the proposed model can outperform the existing models in terms of detection rates.

cs.CV↗

arXiv2018

A Directionally Selective Neural Network with Separated ON and OFF Pathways for Translational Motion Perception in a Visually Cluttered Environment

With respect to biological findings underlying fly's physiology in the past decade, we present a directionally selective neural network, with a feed-forward structure and entirely low-level visual processing, so as to implement direction selective neurons in the fly's visual system, which are mainly sensitive to wide-field translational movements in four cardinal directions. In this research, we highlight the functionality of ON and OFF pathways, separating motion information for parallel computation corresponding to light-on and light-off selectivity. Through this modeling study, we demonstrate several achievements compared with former bio-plausible translational motion detectors, like the elementary motion detectors. First, we thoroughly mimic the fly's preliminary motion-detecting pathways with newly revealed fly's physiology. Second, we improve the speed response to moving dark/light features via the design of ensembles of same polarity cells in the dual-pathways. Moreover, we alleviate the impact of irrelevant motion in a visually cluttered environment like the shifting of background and windblown vegetation, via the modeling of spatiotemporal dynamics. We systematically tested the DSNN against stimuli ranging from synthetic and real-world scenes, to notably a visual modality of a ground micro robot. The results demonstrated that the DSNN outperforms former bio-plausible translational motion detectors. Importantly, we verified its computational simplicity and effectiveness benefiting the building of neuromorphic vision sensor for robots.

cs.NE↗