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A Robust Visual System for Looming Cue Detection Against Translating Motion

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posted on 2024-03-27, 12:31 authored by Fang Lei, Zhiping Peng, Mei Liu, Jigen Peng, Vassilis Cutsuridis, Shigang Yue
Collision detection is critical for autonomous vehicles or robots to serve human society safely. Detecting looming objects robustly and timely plays an important role in collision avoidance systems. The locust lobula giant movement detector (LGMD1) is specifically selective to looming objects which are on a direct collision course. However, the existing LGMD1 models cannot distinguish a looming object from a near and fast translatory moving object, because the latter can evoke a large amount of excitation that can lead to false LGMD1 spikes. This article presents a new visual neural system model (LGMD1) that applies a neural competition mechanism within a framework of separated ON and OFF pathways to shut off the translating response. The competition-based approach responds vigorously to monotonous ON/OFF responses resulting from a looming object. However, it does not respond to paired ON-OFF responses that result from a translating object, thereby enhancing collision selectivity. Moreover, a complementary denoising mechanism ensures reliable collision detection. To verify the effectiveness of the model, we have conducted systematic comparative experiments on synthetic and real datasets. The results show that our method exhibits more accurate discrimination between looming and translational events-the looming motion can be correctly detected. It also demonstrates that the proposed model is more robust than comparative models.

History

Author affiliation

College of Science & Engineering/Comp' & Math' Sciences

Version

  • VoR (Version of Record)

Published in

IEEE Transactions on Neural Networks and Learning Systems

Volume

34

Issue

11

Pagination

8362 - 8376

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

issn

2162-237X

eissn

2162-2388

Copyright date

2022

Available date

2024-03-27

Spatial coverage

United States

Language

eng

Deposited by

Professor Shigang Yue

Deposit date

2024-03-26

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