751 research outputs found
Deep Learning on Home Drone: Searching for the Optimal Architecture
We suggest the first system that runs real-time semantic segmentation via
deep learning on a weak micro-computer such as the Raspberry Pi Zero v2 (whose
price was \16\times\times$ 41 mm). The result is an autonomous drone (no
laptop nor human in the loop) that can detect and classify objects in real-time
from a video stream of an on-board monocular RGB camera (no GPS or LIDAR
sensors). The companion videos demonstrate how this Tello drone scans the lab
for people (e.g. for the use of firefighters or security forces) and for an
empty parking slot outside the lab.
Existing deep learning solutions are either much too slow for real-time
computation on such IoT devices, or provide results of impractical quality. Our
main challenge was to design a system that takes the best of all worlds among
numerous combinations of networks, deep learning platforms/frameworks,
compression techniques, and compression ratios. To this end, we provide an
efficient searching algorithm that aims to find the optimal combination which
results in the best tradeoff between the network running time and its
accuracy/performance
SAR ATR under Limited Training Data Via MobileNetV3
In recent years, deep learning has been widely used to solve the bottleneck
problem of synthetic aperture radar (SAR) automatic target recognition (ATR).
However, most current methods rely heavily on a large number of training
samples and have many parameters which lead to failure under limited training
samples. In practical applications, the SAR ATR method needs not only superior
performance under limited training data but also real-time performance.
Therefore, we try to use a lightweight network for SAR ATR under limited
training samples, which has fewer parameters, less computational effort, and
shorter inference time than normal networks. At the same time, the lightweight
network combines the advantages of existing lightweight networks and uses a
combination of MnasNet and NetAdapt algorithms to find the optimal neural
network architecture for a given problem. Through experiments and comparisons
under the moving and stationary target acquisition and recognition (MSTAR)
dataset, the lightweight network is validated to have excellent recognition
performance for SAR ATR on limited training samples and be very computationally
small, reflecting the great potential of this network structure for practical
applications.Comment: 6 pages, 3 figures, published in 2023 IEEE Radar Conference
(RadarConf23
MoGA: Searching Beyond MobileNetV3
The evolution of MobileNets has laid a solid foundation for neural network
applications on mobile end. With the latest MobileNetV3, neural architecture
search again claimed its supremacy in network design. Unfortunately, till today
all mobile methods mainly focus on CPU latencies instead of GPU, the latter,
however, is much preferred in practice for it has faster speed, lower overhead
and less interference. Bearing the target hardware in mind, we propose the
first Mobile GPU-Aware (MoGA) neural architecture search in order to be
precisely tailored for real-world applications. Further, the ultimate objective
to devise a mobile network lies in achieving better performance by maximizing
the utilization of bounded resources. Urging higher capability while
restraining time consumption is not reconcilable. We alleviate the tension by
weighted evolution techniques. Moreover, we encourage increasing the number of
parameters for higher representational power. With 200x fewer GPU days than
MnasNet, we obtain a series of models that outperform MobileNetV3 under the
similar latency constraints, i.e., MoGA-A achieves 75.9% top-1 accuracy on
ImageNet, MoGA-B meets 75.5% which costs only 0.5 ms more on mobile GPU. MoGA-C
best attests GPU-awareness by reaching 75.3% and being slower on CPU but faster
on GPU.The models and test code is made available here
https://github.com/xiaomi-automl/MoGA.Comment: Accepted by ICASSP202
- …