42,038 research outputs found
Efficient Fully Convolution Neural Network for Generating Pixel Wise Robotic Grasps With High Resolution Images
This paper presents an efficient neural network model to generate robotic
grasps with high resolution images. The proposed model uses fully convolution
neural network to generate robotic grasps for each pixel using 400 400
high resolution RGB-D images. It first down-sample the images to get features
and then up-sample those features to the original size of the input as well as
combines local and global features from different feature maps. Compared to
other regression or classification methods for detecting robotic grasps, our
method looks more like the segmentation methods which solves the problem
through pixel-wise ways. We use Cornell Grasp Dataset to train and evaluate the
model and get high accuracy about 94.42% for image-wise and 91.02% for
object-wise and fast prediction time about 8ms. We also demonstrate that
without training on the multiple objects dataset, our model can directly output
robotic grasps candidates for different objects because of the pixel wise
implementation.Comment: Submitted to ROBIO 201
Cloud Chaser: Real Time Deep Learning Computer Vision on Low Computing Power Devices
Internet of Things(IoT) devices, mobile phones, and robotic systems are often
denied the power of deep learning algorithms due to their limited computing
power. However, to provide time-critical services such as emergency response,
home assistance, surveillance, etc, these devices often need real-time analysis
of their camera data. This paper strives to offer a viable approach to
integrate high-performance deep learning-based computer vision algorithms with
low-resource and low-power devices by leveraging the computing power of the
cloud. By offloading the computation work to the cloud, no dedicated hardware
is needed to enable deep neural networks on existing low computing power
devices. A Raspberry Pi based robot, Cloud Chaser, is built to demonstrate the
power of using cloud computing to perform real-time vision tasks. Furthermore,
to reduce latency and improve real-time performance, compression algorithms are
proposed and evaluated for streaming real-time video frames to the cloud.Comment: Accepted to The 11th International Conference on Machine Vision (ICMV
2018). Project site: https://zhengyiluo.github.io/projects/cloudchaser
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