Computational model of the LGMD neuron for automatic collision detection

Silva, A.; Santos, Cristina P.

doi:10.1109/enbeng.2013.6518420

Cited by 5 publications

(5 citation statements)

References 10 publications

(11 reference statements)

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“…The experimental data is adapted from [101]. model encoding onset and offset responses by luminance increments and decrements, adapted from [114], (b) a modified LGMD1 model for multiple looming objects detection, adapted from [233], (c) a simplified LGMD1 model for collision avoidance of an UAV, adapted from [188], (d) a modified LGMD1 model with enhancement of collision selectivity, adapted from [133,132], (e) a modified LGMD1 model with a new layer for noise reduction and spikingthreshold mediation, adapted from [198,197], (f) an LGMD1 neural network based on the modelling of elementary motion detectors for collision detection in ground vehicle scenarios, adapted from [91]. Based on this LGMD1 modelling theory, a good number of models have been produced during the past two decades; these works have not only been extending and consolidating the LGMD1's original functionality for looming perception, but also investigating the possible applications to mobile machines like robots and vehicles.…”

Section: Computational Models and Applicationsmentioning

confidence: 99%

“…10. These computational models consist of new methods to enhance the collision selectivity to approaching objects [133], new layers to reduce environmental noise [198,197], and etc.There are also researches on corresponding applications for cars [124,91] and mobile robots [43], as well as implementations in hardware like field-programmable gate array (FPGA) [132].…”

Section: Computational Models and Applicationsmentioning

confidence: 99%

“…Figure 10: Schematics of a variety of LGMD1-based visual neural networks: (a) an LGMD1 model encoding onset and offset responses by luminance increments and decrements, adapted from [114], (b) a modified LGMD1 model for multiple looming objects detection, adapted from [233], (c) a simplified LGMD1 model for collision avoidance of an UAV, adapted from [188], (d) a modified LGMD1 model with enhancement of collision selectivity, adapted from[133,132], (e) a modified LGMD1 model with a new layer for noise reduction and spikingthreshold mediation, adapted from[198,197], (f) an LGMD1 neural network based on the modelling of elementary motion detectors for collision detection in ground vehicle scenarios, adapted from[91].…”

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confidence: 99%

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

Wang

et al. 2019

Artificial Life

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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.

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Section: Computational Models and Applicationsmentioning

confidence: 99%

Section: Computational Models and Applicationsmentioning

confidence: 99%

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confidence: 99%

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

Wang

et al. 2019

Artificial Life

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“…To realize the neural characteristics of LGMDs, a few computational models have been proposed for LGMD1 [1], [19], [20], [23], [30] and successfully utilized in vision-based platforms such as vehicles [14], [15] and robots [3], [16]- [18], [24], [31] for collision detection. Nevertheless, very little modeling works have been conducted for LGMD2.…”

Section: A Lgmds Neural Characteristics and Modelsmentioning

confidence: 99%

Collision Selective Visual Neural Network Inspired by LGMD2 Neurons in Juvenile Locusts

Fu,

Hu,

Yue

2018

Preprint

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For autonomous robots in dynamic environments mixed with human, it is vital to detect impending collision quickly and robustly. The biological visual systems evolved over millions of years may provide us efficient solutions for collision detection in complex environments. In the cockpit of locusts, two Lobula Giant Movement Detectors, i.e. LGMD1 and LGMD2, have been identified which respond to looming objects rigorously with high firing rates. Compared to LGMD1, LGMD2 matures early in the juvenile locusts with specific selectivity to dark moving objects against bright background in depth while not responding to light objects embedded in dark background -a similar situation which ground vehicles and robots are facing with. However, little work has been done on modeling LGMD2, let alone its potential in robotics and other vision-based applications. In this article, we propose a novel way of modeling LGMD2 neuron, with biased ON and OFF pathways splitting visual streams into parallel channels encoding brightness increments and decrements separately to fulfill its selectivity. Moreover, we apply a biophysical mechanism of spike frequency adaptation to shape the looming selectivity in such a collision-detecting neuron model. The proposed visual neural network has been tested with systematic experiments, challenged against synthetic and real physical stimuli, as well as image streams from the sensor of a miniature robot. The results demonstrated this framework is able to detect looming dark objects embedded in bright backgrounds selectively, which make it ideal for ground mobile platforms. The robotic experiments also showed its robustness in collision detection -it performed well for near range navigation in an arena with many obstacles. Its enhanced collision selectivity to dark approaching objects versus receding and translating ones has also been verified via systematic experiments.

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“…Such a fascinating neuron has been computationally modelled as collision selective visual neural networks or models (e.g. [2,11,17,19]), and applied in mobile machines like ground robots [5,7,10] and UAVs [15], and also embodied in hardware implementations like the FPGA [12]. These works have partially reproduced the LGMD's responses and demonstrated that it features efficient neural computation for quick and reliable looming or collision sensing.…”

Section: Introductionmentioning

confidence: 99%

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

Bellotto

Wang

et al. 2019

Preprint

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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.

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Computational model of the LGMD neuron for automatic collision detection

Cited by 5 publications

References 10 publications

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

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

Collision Selective Visual Neural Network Inspired by LGMD2 Neurons in Juvenile Locusts

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

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