Efficient Object Detection in Autonomous Driving using Spiking Neural Networks: Performance, Energy Consumption Analysis, and Insights into Open-set Object Discovery
CoRR(2023)
摘要
Besides performance, efficiency is a key design driver of technologies
supporting vehicular perception. Indeed, a well-balanced trade-off between
performance and energy consumption is crucial for the sustainability of
autonomous vehicles. In this context, the diversity of real-world contexts in
which autonomous vehicles can operate motivates the need for empowering
perception models with the capability to detect, characterize and identify
newly appearing objects by themselves. In this manuscript we elaborate on this
threefold conundrum (performance, efficiency and open-world learning) for
object detection modeling tasks over image data collected from vehicular
scenarios. Specifically, we show that well-performing and efficient models can
be realized by virtue of Spiking Neural Networks (SNNs), reaching competitive
levels of detection performance when compared to their non-spiking counterparts
at dramatic energy consumption savings (up to 85%) and a slightly improved
robustness against image noise. Our experiments herein offered also expose
qualitatively the complexity of detecting new objects based on the preliminary
results of a simple approach to discriminate potential object proposals in the
captured image.
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关键词
Energy Consumption,Object Detection,Autonomous Vehicles,Spiking Neural Networks,Energy Consumption Analysis,Efficient Object Detection,Image Noise,Convolutional Neural Network,Artificial Neural Network,Simulation Time,Feature Maps,Bounding Box,Inference Time,Number Of Time Steps,Faster R-CNN,Spike Trains,Spatial Level,Feature Alignment,Feature Pyramid Network,Region Proposal Network,Unknown Objects,Two-stage Detectors,Spike-timing-dependent Plasticity,Backpropagation Through Time,Spatial Depth,Neuromorphic Hardware,Object Detection Model,Floating-point Operations,Energy Conservation,One-stage Detectors
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