Edgars Lielāmurs, Kaspars Ozols. Spatio-temporal Object Detection with Deep Spiking CNNs Using Time-of-Flight Data. Baltic Electronics Conference 2024, IEEE, 2024.
Bibtex citāts:
Bibtex citāts:
@inproceedings{16868_2024,
author = {Edgars Lielāmurs and Kaspars Ozols},
title = {Spatio-temporal Object Detection with Deep Spiking CNNs Using Time-of-Flight Data},
journal = {Baltic Electronics Conference 2024},
publisher = {IEEE},
year = {2024}
}
author = {Edgars Lielāmurs and Kaspars Ozols},
title = {Spatio-temporal Object Detection with Deep Spiking CNNs Using Time-of-Flight Data},
journal = {Baltic Electronics Conference 2024},
publisher = {IEEE},
year = {2024}
}
Anotācija: Perceiving the surrounding environment with vision
sensors, including LiDAR and direct time-of-flight (dToF), is
a common task across several domains, such as autonomous
vehicles, industrial automation and robotics. As sensors and
perception algorithms continue to evolve, their applications are
likely to expand further and put more strain on computing
resources, necessitating more efficient processing. Neuromorphic
computing is a promising solution for efficiently handling sparse
event streams, aligning well with the characteristics of sparse
LiDAR sensory data. Moreover, the advent of dedicated neuro-
morphic vision processors and efficient spike backpropagation
training through surrogate gradient (SG) is further inspiring
the development of Spiking Neural Networks (SNNs). Thus, in
this work, we take advantage of sparse LiDAR sensor point
cloud data by formalizing a temporal spike encoding method
and implement a 3D object detection convolutional SNN. We
conducted comprehensive experiments on the KITTI automotive
dataset, showing that the proposed model outperforms closely
related spiking neural network solutions and approaches conven-
tional state-of-the-art solution performance. More importantly,
achieving a mean sparsity of 55.73% underlines the potential of
using SNNs for a more efficient way of processing time-of-flight
Data.