The DOI matches its Crossref record; 43 of the 43 references it cites were found in a registry or an index.
Slim-neck by GSConv: a lightweight-design for real-time detector architectures
Open accessAbstract
Real-time object detection is significant for industrial and research fields. On edge devices, a giant model is difficult to achieve the real-time detecting requirement, and a lightweight model built from a large number of the depth-wise separable convolutional could not achieve the sufficient accuracy. We introduce a new lightweight convolutional technique, GSConv, to lighten the model but maintain the accuracy. The GSConv accomplishes an excellent trade-off between the accuracy and speed. Furthermore, we provide a design suggestion based on the GSConv, slim-neck (SNs), to achieve a higher computational cost-effectiveness of the real-time detectors. The effectiveness of the SNs was robustly demonstrated in over twenty sets comparative experiments. In particular, the real-time detectors of ameliorated by the SNs obtain the state-of-the-art (70.9% AP50 for the SODA10M at a speed of ~ 100 FPS on a Tesla T4) compared with the baselines. Code is available at https://github.com/alanli1997/slim-neck-by-gsconv.
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Deep Residual Learning for Image Recognition
He, K., Zhang, X., Ren, S., & Sun, J. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.90 · Cited by 235,474
ImageNet classification with deep convolutional neural networks
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Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
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You Only Look Once: Unified, Real-Time Object Detection
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Self-citationRich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation
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Cited by
Zhang, S., Wang, K., Zhang, H., Wang, T., Gao, X., Song, Y., & Wang, F. (2024) · Composites Part A: Applied Science and Manufacturing · doi:10.1016/j.compositesa.2024.108337 · Cited by 17
Metadata
- Authors
- Li, H., Li, J., Wei, H., Liu, Z., Zhan, Z., & Ren, Q.
- Venue
- Journal of Real-Time Image Processing
- Published
- 2024-03-29
- Volume
- 21
- Issue
- 3
- ISSN
- 1861-8200 (ISSN-L)
- 1861-8219
- OpenAlex
- W4393308750
- Cited by
- 886
- References
- 43
- FWCI
- 115.97
- Type
- journal article
- Language
- en
- Open access
- green
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