214,218 research outputs found
Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
State-of-the-art object detection networks depend on region proposal
algorithms to hypothesize object locations. Advances like SPPnet and Fast R-CNN
have reduced the running time of these detection networks, exposing region
proposal computation as a bottleneck. In this work, we introduce a Region
Proposal Network (RPN) that shares full-image convolutional features with the
detection network, thus enabling nearly cost-free region proposals. An RPN is a
fully convolutional network that simultaneously predicts object bounds and
objectness scores at each position. The RPN is trained end-to-end to generate
high-quality region proposals, which are used by Fast R-CNN for detection. We
further merge RPN and Fast R-CNN into a single network by sharing their
convolutional features---using the recently popular terminology of neural
networks with 'attention' mechanisms, the RPN component tells the unified
network where to look. For the very deep VGG-16 model, our detection system has
a frame rate of 5fps (including all steps) on a GPU, while achieving
state-of-the-art object detection accuracy on PASCAL VOC 2007, 2012, and MS
COCO datasets with only 300 proposals per image. In ILSVRC and COCO 2015
competitions, Faster R-CNN and RPN are the foundations of the 1st-place winning
entries in several tracks. Code has been made publicly available.Comment: Extended tech repor
Accurate Pulmonary Nodule Detection in Computed Tomography Images Using Deep Convolutional Neural Networks
Early detection of pulmonary cancer is the most promising way to enhance a
patient's chance for survival. Accurate pulmonary nodule detection in computed
tomography (CT) images is a crucial step in diagnosing pulmonary cancer. In
this paper, inspired by the successful use of deep convolutional neural
networks (DCNNs) in natural image recognition, we propose a novel pulmonary
nodule detection approach based on DCNNs. We first introduce a deconvolutional
structure to Faster Region-based Convolutional Neural Network (Faster R-CNN)
for candidate detection on axial slices. Then, a three-dimensional DCNN is
presented for the subsequent false positive reduction. Experimental results of
the LUng Nodule Analysis 2016 (LUNA16) Challenge demonstrate the superior
detection performance of the proposed approach on nodule detection(average
FROC-score of 0.891, ranking the 1st place over all submitted results).Comment: MICCAI 2017 accepte
SPARCNN: SPAtially Related Convolutional Neural Networks
The ability to accurately detect and classify objects at varying pixel sizes
in cluttered scenes is crucial to many Navy applications. However, detection
performance of existing state-of the-art approaches such as convolutional
neural networks (CNNs) degrade and suffer when applied to such cluttered and
multi-object detection tasks. We conjecture that spatial relationships between
objects in an image could be exploited to significantly improve detection
accuracy, an approach that had not yet been considered by any existing
techniques (to the best of our knowledge) at the time the research was
conducted. We introduce a detection and classification technique called
Spatially Related Detection with Convolutional Neural Networks (SPARCNN) that
learns and exploits a probabilistic representation of inter-object spatial
configurations within images from training sets for more effective region
proposals to use with state-of-the-art CNNs. Our empirical evaluation of
SPARCNN on the VOC 2007 dataset shows that it increases classification accuracy
by 8% when compared to a region proposal technique that does not exploit
spatial relations. More importantly, we obtained a higher performance boost of
18.8% when task difficulty in the test set is increased by including highly
obscured objects and increased image clutter.Comment: 6 pages, AIPR 2016 submissio
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