8,424 research outputs found
Towards lightweight convolutional neural networks for object detection
We propose model with larger spatial size of feature maps and evaluate it on
object detection task. With the goal to choose the best feature extraction
network for our model we compare several popular lightweight networks. After
that we conduct a set of experiments with channels reduction algorithms in
order to accelerate execution. Our vehicle detection models are accurate, fast
and therefore suit for embedded visual applications. With only 1.5 GFLOPs our
best model gives 93.39 AP on validation subset of challenging DETRAC dataset.
The smallest of our models is the first to achieve real-time inference speed on
CPU with reasonable accuracy drop to 91.43 AP.Comment: Submitted to the International Workshop on Traffic and Street
Surveillance for Safety and Security (IWT4S) in conjunction with the 14th
IEEE International Conference on Advanced Video and Signal based Surveillance
(AVSS 2017
Mobile Video Object Detection with Temporally-Aware Feature Maps
This paper introduces an online model for object detection in videos designed
to run in real-time on low-powered mobile and embedded devices. Our approach
combines fast single-image object detection with convolutional long short term
memory (LSTM) layers to create an interweaved recurrent-convolutional
architecture. Additionally, we propose an efficient Bottleneck-LSTM layer that
significantly reduces computational cost compared to regular LSTMs. Our network
achieves temporal awareness by using Bottleneck-LSTMs to refine and propagate
feature maps across frames. This approach is substantially faster than existing
detection methods in video, outperforming the fastest single-frame models in
model size and computational cost while attaining accuracy comparable to much
more expensive single-frame models on the Imagenet VID 2015 dataset. Our model
reaches a real-time inference speed of up to 15 FPS on a mobile CPU.Comment: In CVPR 201
Receptive Field Block Net for Accurate and Fast Object Detection
Current top-performing object detectors depend on deep CNN backbones, such as
ResNet-101 and Inception, benefiting from their powerful feature
representations but suffering from high computational costs. Conversely, some
lightweight model based detectors fulfil real time processing, while their
accuracies are often criticized. In this paper, we explore an alternative to
build a fast and accurate detector by strengthening lightweight features using
a hand-crafted mechanism. Inspired by the structure of Receptive Fields (RFs)
in human visual systems, we propose a novel RF Block (RFB) module, which takes
the relationship between the size and eccentricity of RFs into account, to
enhance the feature discriminability and robustness. We further assemble RFB to
the top of SSD, constructing the RFB Net detector. To evaluate its
effectiveness, experiments are conducted on two major benchmarks and the
results show that RFB Net is able to reach the performance of advanced very
deep detectors while keeping the real-time speed. Code is available at
https://github.com/ruinmessi/RFBNet.Comment: Accepted by ECCV 201
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