SqueezeDet: Unified, Small, Low Power Fully Convolutional Neural Networks for Real-Time Object Detection for Autonomous Driving

Abstract

Object detection is a crucial task for autonomous driving. In addition to requiring high accuracy to ensure safety, object detection for autonomous driving also requires realtime inference speed to guarantee prompt vehicle control, as well as small model size and energy efficiency to enable embedded system deployment.,,,,,, In this work, we propose SqueezeDet, a fully convolutional neural network for object detection that aims to simultaneously satisfy all of the above constraints. In our network we use convolutional layers not only to extract feature maps, but also as the output layer to compute bounding boxes and class probabilities. The detection pipeline of our model only contains a single forward pass of a neural network, thus it is extremely fast. Our model is fullyconvolutional, which leads to small model size and better energy efficiency. Finally, our experiments show that our model is very accurate, achieving state-of-the-art accuracy on the KITTI [10] benchmark. The source code of SqueezeDet is open-source released.

DOI: 10.1109/CVPRW.2017.60

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Cite this paper

@article{Wu2017SqueezeDetUS, title={SqueezeDet: Unified, Small, Low Power Fully Convolutional Neural Networks for Real-Time Object Detection for Autonomous Driving}, author={Bichen Wu and Forrest N. Iandola and Peter H. Jin and Kurt Keutzer}, journal={2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)}, year={2017}, pages={446-454} }