The DOI matches its Crossref record; 30 of the 30 references it cites were found in a registry or an index.
An Energy and GPU-Computation Efficient Backbone Network for Real-Time Object Detection
Open accessAbstract
As DenseNet conserves intermediate features with diverse receptive fields by aggregating them with dense connection, it shows good performance on the object detection task. Although feature reuse enables DenseNet to produce strong features with a small number of model parameters and FLOPs, the detector with DenseNet backbone shows rather slow speed and low energy efficiency. We find the linearly increasing input channel by dense connection leads to heavy memory access cost, which causes computation overhead and more energy consumption. To solve the inefficiency of DenseNet, we propose an energy and computation efficient architecture called VoVNet comprised of One-Shot Aggregation (OSA). The OSA not only adopts the strength of DenseNet that represents diversified features with multi receptive fields but also overcomes the inefficiency of dense connection by aggregating all features only once in the last feature maps. To validate the effectiveness of VoVNet as a backbone network, we design both lightweight and large-scale VoVNet and apply them to one-stage and two-stage object detectors. Our VoVNet based detectors outperform DenseNet based ones with 2× faster speed and the energy consumptions are reduced by 1.6× - 4.1×. In addition to DenseNet, VoVNet also outperforms widely used ResNet backbone with faster speed and better energy efficiency. In particular, the small object detection performance has been significantly improved over DenseNet and ResNet.
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Cites
Deep Residual Learning for Image Recognition
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Very Deep Convolutional Networks for Large-Scale Image Recognition
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Densely Connected Convolutional Networks
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Going deeper with convolutions
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Microsoft COCO: Common Objects in Context
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Feature Pyramid Networks for Object Detection
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Focal Loss for Dense Object Detection
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MobileNetV2: Inverted Residuals and Linear Bottlenecks
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Cited by
YOLOv7: Trainable Bag-of-Freebies Sets New State-of-the-Art for Real-Time Object Detectors
Wang, C.-Y., Bochkovskiy, A., & Liao, H.-Y. M. (2023) · 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52729.2023.00721 · Cited by 13,198
“The design of CSPVoVNet [79] in Figure 2 (b) is a variation of VoVNet [39].”
MethodInfluentialYOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors
Wang, C.-Y., Bochkovskiy, A., & Liao, H.-Y. M. (2022) · arXiv · doi:10.48550/arxiv.2207.02696 · Cited by 13,205
“The design of CSPVoVNet [79] in Figure 2 (b) is a variation of VoVNet [39].”
MethodInfluentialReview of Image Classification Algorithms Based on Convolutional Neural Networks
Chen, L., Li, S., Bai, Q., Yang, J., Jiang, S., & Miao, Y. (2021) · Remote Sensing · doi:10.3390/rs13224712 · Cited by 789
CenterMask: Real-Time Anchor-Free Instance Segmentation
Lee, Y., & Park, J. (2020) · 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr42600.2020.01392 · Cited by 767
Self-citationDC-YOLOv8: Small-Size Object Detection Algorithm Based on Camera Sensor
Lou, H., Duan, X., Guo, J., Liu, H., Gu, J., Bi, L., & Chen, H. (2023) · Electronics · doi:10.3390/electronics12102323 · Cited by 630
Metadata
- Authors
- Lee, Y., Hwang, J.-W., Lee, S., Bae, Y., & Park, J.
- Venue
- 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)
- Publisher
- IEEE
- Published
- 2019-06
- Pages
- 752-760
- OpenAlex
- W2935758458
- Cited by
- 508
- References
- 30
- FWCI
- 9.95
- Type
- conference paper
- Language
- en
- Open access
- green
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- Lee, Y., Hwang, J.-W., Lee, S., Bae, Y., & Park, J. (2019). An Energy and GPU-Computation Efficient Backbone Network for Real-Time Object Detection. 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW). https://doi.org/10.1109/cvprw.2019.00103
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