SqueezeDet: Unified, Small, Low Power Fully Convolutional Neural Networks for Real-Time Object Detection for Autonomous Driving
Open accessAbstract
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 fully-convolutional, 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 benchmark. The source code of SqueezeDet is open-source released.
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Traditional TinyML model compression techniques can lead to significant accuracy loss due to poor matrix characteristics resulting from high compression rates.
“However, the traditional techniques for compressing TinyML models mentioned above can lead to a significant loss of accuracy due to poor matrix characteristics resulting from high compression rates [52].”
From A Comprehensive Survey on TinyML (2023) · cites this paper as [52]
The bottle-neck design has been widely used for designing lightweight networks.
“In recent years, the bottle-neck design has been widely used for designing lightweight networks [50, 133, 146-148].”
From Object Detection in 20 Years: A Survey (2023) · cites this paper as [147]
The input layer of a detector can be compressed to reduce the amount of computation from the very beginning of detection.
“Among these methods, the input layer of a detector can be compressed to reduce the amount of computation from the very beginning of the detection [133, 146, 147].”
From Object Detection in 20 Years: A Survey (2023) · cites this paper as [147]
Edge nodes can serve as a cache server or transcoding server to provide edge-based (or edge-cloudbased) adaptive streaming.
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From Deep learning for edge computing applications: A State-of-the-Art survey (2020) · cites this paper as [84]
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- Authors
- Wu, B., Wan, A., Iandola, F., Jin, P. H., & Keutzer, K.
- Affiliations
- University of California, Berkeley (US)
- Venue
- 2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)
- Publisher
- IEEE
- Published
- 2017-07
- Pages
- 446-454
- OpenAlex
- W2963087201
- Cited by
- 614
- References
- 23
- Citations by year
- 2026: 17 · 2025: 38 · 2024: 45 · 2023: 75 · 2022: 84 · 2021: 100 · 2020: 100 · 2019: 93 · 2018: 53 · 2017: 8 · 2016: 1
- FWCI
- 19.99
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Physical Sciences › Engineering › Automotive Engineering › Autonomous Vehicle Technology and Safety
- Physical Sciences › Computer Science › Artificial Intelligence › Domain Adaptation and Few-Shot Learning
- Keywords
- object detection, autonomous driving, fully convolutional network, real-time inference, energy efficiency, model compression, embedded systems, bounding box regression, KITTI dataset
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