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

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.