Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Deep SCNN-based Real-time Object Detection for Self-driving Vehicles
  Using LiDAR Temporal Data   Real-time accurate detection of three-dimensional (3D) objects is a
fundamental necessity for self-driving vehicles. Most existing computer vision
approaches are based on convolutional neural networks (CNNs). Although the
CNN-based approaches can achieve high detection accuracy, their high energy
consumption is a severe drawback. To resolve this problem, novel energy
efficient approaches should be explored. Spiking neural network (SNN) is a
promising candidate because it has orders-of-magnitude lower energy consumption
than CNN. Unfortunately, the studying of SNN has been limited in small networks
only. The application of SNN for large 3D object detection networks has remain
largely open. In this paper, we integrate spiking convolutional neural network
(SCNN) with temporal coding into the YOLOv2 architecture for real-time object
detection. To take the advantage of spiking signals, we develop a novel data
preprocessing layer that translates 3D point-cloud data into spike time data.
We propose an analog circuit to implement the non-leaky integrate and fire
neuron used in our SCNN, from which the energy consumption of each spike is
estimated. Moreover, we present a method to calculate the network sparsity and
the energy consumption of the overall network. Extensive experiments have been
conducted based on the KITTI dataset, which show that the proposed network can
reach competitive detection accuracy as existing approaches, yet with much
lower average energy consumption. If implemented in dedicated hardware, our
network could have a mean sparsity of 56.24% and extremely low total energy
consumption of 0.247mJ only. Implemented in NVIDIA GTX 1080i GPU, we can
achieve 35.7 fps frame rate, high enough for real-time object detection.

23
The categories of this paper are: solv-int
22
The categories of this paper are: chao-dyn
37
The categories of this paper are: supr-con
36
The categories of this paper are: cmp-lg
31
The categories of this paper are: ao-sci
8
The categories of this paper are: hep-ex
27
The categories of this paper are: comp-gas
13
The categories of this paper are: quant-ph
3
The categories of this paper are: physics
32
The categories of this paper are: atom-ph
0
The categories of this paper are: hep-ph
24
The categories of this paper are: dg-ga
30
The categories of this paper are: q-alg
7
The categories of this paper are: hep-th
5
The categories of this paper are: gr-qc
17
The categories of this paper are: q-fin