Slim-neck by GSConv: a lightweight-design for real-time detector architectures
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 accur...
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| Published in | Journal of real-time image processing Vol. 21; no. 3; p. 62 |
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| Main Authors | , , , , , |
| Format | Journal Article |
| Language | English |
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Berlin/Heidelberg
Springer Berlin Heidelberg
01.05.2024
Springer Nature B.V |
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| Abstract | 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% AP
50
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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| AbstractList | 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% AP
50
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
. 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. |
| ArticleNumber | 62 |
| Author | Li, Jun Wei, Hanbing Zhan, Zhenfei Li, Hulin Liu, Zheng Ren, Qiliang |
| Author_xml | – sequence: 1 givenname: Hulin surname: Li fullname: Li, Hulin email: alan@mails.cqjtu.edu.cn organization: College of Traffic and Transportation, Chongqing Jiaotong University – sequence: 2 givenname: Jun surname: Li fullname: Li, Jun organization: School of Mechatronics and Vehicle Engineering, Chongqing Jiaotong University – sequence: 3 givenname: Hanbing surname: Wei fullname: Wei, Hanbing organization: School of Mechatronics and Vehicle Engineering, Chongqing Jiaotong University – sequence: 4 givenname: Zheng surname: Liu fullname: Liu, Zheng organization: School of Engineering, University of British Columbia Okanagan – sequence: 5 givenname: Zhenfei surname: Zhan fullname: Zhan, Zhenfei organization: School of Mechatronics and Vehicle Engineering, Chongqing Jiaotong University – sequence: 6 givenname: Qiliang surname: Ren fullname: Ren, Qiliang organization: College of Traffic and Transportation, Chongqing Jiaotong University |
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| SubjectTerms | Accuracy Computational efficiency Computer Graphics Computer Science Design Detectors Effectiveness Image Processing and Computer Vision Lightweight Multimedia Information Systems Neural networks Object recognition Pattern Recognition Real time Sensors Signal,Image and Speech Processing Weight reduction |
| Title | Slim-neck by GSConv: a lightweight-design for real-time detector architectures |
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