Spiking Neural Networks for Robust and Efficient Object Detection in Intelligent Transportation Systems With Roadside Event-Based Cameras

Mikihiro Ikura, Florian Walter, Alois Knoll

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Object detection is a key technology for intelligent transportation systems (ITSs) to recognize surrounding vehicles. Robust and efficient object detection with roadside sensors could make them more sustainable. This research uses the CARLA simulator to generate synthetic datasets from roadside event-based cameras with multiple weather conditions and evaluates Spiking Neural Networks (SNNs) to improve the sustainability with these datasets. Event-based cameras can detect the change of each pixel intensity asynchronously even under adverse environments such as night. In addition, SNNs have lower energy consumption with neuromorphic hardware than conventional CNNs and can process time-continuous data including event-based data. Evaluations in this research indicate that fine-tuning of YOLOv5 with accumulated event images improves the robustness against adverse weather conditions and SNNs with raw event-based datasets reduce both energy consumption and computational time. Furthermore, the event polarities made object detection more robust against the motion direction of vehicles.

Original languageEnglish
Title of host publicationIV 2023 - IEEE Intelligent Vehicles Symposium, Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9798350346916
DOIs
StatePublished - 2023
Event34th IEEE Intelligent Vehicles Symposium, IV 2023 - Anchorage, United States
Duration: 4 Jun 20237 Jun 2023

Publication series

NameIEEE Intelligent Vehicles Symposium, Proceedings
Volume2023-June

Conference

Conference34th IEEE Intelligent Vehicles Symposium, IV 2023
Country/TerritoryUnited States
CityAnchorage
Period4/06/237/06/23

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