ICAN2022

Yicheng Zhang Presents at ICANN 2022

Artificial Neural Networks and Machine Learning (ICANN) 2022 is the 31st International Conference on Artificial Neural Networks. The conference was organised by the Department of Computer Science and Creative Technologies of the University of the West of England at Frenchay Campus, Bristol, from 6 to 9 September 2022. It was held in a hybrid mode with delegates attending on-site and remotely via an immersive online space.

ICAN2022

This conference featured two main tracks: Brain inspired computing and Machine learning research, with strong cross-disciplinary interactions and applications. The event attracted a large number and wide range of new and established researchers from five continents and 27 countries in total. The research themes explored all innovative pathways in the wider area of Neural Networks and Machine Learning. 561 papers were submitted with 259 selected to be presented orally at the conference.

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ULTRACEPT researcher Yicheng Zhang presented his research Zhang, Y. et al. (2022). O-LGMD: An Opponent Colour LGMD-Based Model for Collision Detection with Thermal Images at Night. In: Pimenidis, E., Angelov, P., Jayne, C., Papaleonidas, A., Aydin, M. (eds) Artificial Neural Networks and Machine Learning – ICANN 2022. ICANN 2022. Lecture Notes in Computer Science, vol 13531. Springer, Cham. https://doi.org/10.1007/978-3-031-15934-3_21. This was presented in session 41 of the conference.

O-LGMD: An Opponent Colour LGMD-Based Model for Collision Detection with Thermal Images at Night

O-LGMD: An Opponent Colour LGMD-Based Model for Collision Detection with Thermal Images at Night

Abstract

It is an enormous challenge for intelligent robots or vehicles to detect and avoid collisions at night because of poor lighting conditions. Thermal cameras capture night scenes with temperature maps, often showing different pseudo-colour modes to enhance the visual effects for the human eyes. Since the features of approaching objects could have been well enhanced in the pseudo-colour outputs of a thermal camera, it is likely that colour cues could help the Lobula Giant Motion Detector (LGMD) to pick up the collision cues effectively. However, there is no investigation published on this aspect and it is not clear whether LGMD-like neural networks can take pseudo-colour information as input for collision detection in extreme dim conditions. In this study, we investigate a few thermal pseudo-colour modes and propose to extract colour cues with a triple-channel LGMD-based neural network to directly process the pseudo-colour images. The proposed model consists of three sub-networks, each dealing with one specific opponent colour channel, i.e. black-white, red-green, or yellow-blue. A collision alarm is triggered if any channel’s output exceeds its threshold for a few successive frames. Our experiments demonstrate that the proposed bio-inspired collision detection system works well in quickly detecting colliding objects in direct collision course in extremely low lighting conditions. The proposed method showed its potential to be part of sensor systems for future robots or vehicles driving at night or in other extreme lighting conditions to help avoiding fatal collisions.

O-LGMD: An Opponent Colour LGMD-Based Model for Collision Detection with Thermal Images at Night

Yicheng was very grateful for the opportunity to attend this fantastic conference with support from ULTRACEPT.

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