Fast and Efficient Non-Contact Ball Detector for Picking Robots
Object detectors based on deep learning requires high-performance computing and large run-time memory footprint to maintain good detection performance. They bring high computation overhead and power consumption to on-board embedded devices of non-contact ball picking robot. Furthermore, it is diffic...
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Format: | Article |
Language: | English |
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IEEE
2019-01-01
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Series: | IEEE Access |
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Online Access: | https://ieeexplore.ieee.org/document/8911435/ |
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author | Qi-Chao Mao Hong-Mei Sun Yan-Bo Liu Rui-Sheng Jia |
author_facet | Qi-Chao Mao Hong-Mei Sun Yan-Bo Liu Rui-Sheng Jia |
author_sort | Qi-Chao Mao |
collection | DOAJ |
description | Object detectors based on deep learning requires high-performance computing and large run-time memory footprint to maintain good detection performance. They bring high computation overhead and power consumption to on-board embedded devices of non-contact ball picking robot. Furthermore, it is difficult to deploy on the machine because the model size is so big. The accuracy of the existing simplified detectors deployed on embedded devices cannot meet the requirements of practical applications. Therefore, how to reduce floating point operations (FLOPs) and the size of model without notably sacrificing detection precision becomes an urgent problem to be solved. To solve this problem, a shuttle residual block which is more efficient network unit based on depthwise separable convolution was proposed. And we designed a non-contact ball object detector for picking robots, which is shallower than YOLOv3 and has narrower structure. We evaluate the proposed method on non-contact Ball dataset and compelling results are achieved by the proposed method. Compared with YOLOv3, the proposed method reduces FLOPs by 86.2%, declines parameter size by 89.5%. Overall, the proposed method achieves comparable detection accuracy than YOLOv3, and its speed is 2.2 times faster than YOLOv3. |
first_indexed | 2024-12-16T17:21:58Z |
format | Article |
id | doaj.art-c7da34cf048f459dbfa035dd74e607d7 |
institution | Directory Open Access Journal |
issn | 2169-3536 |
language | English |
last_indexed | 2024-12-16T17:21:58Z |
publishDate | 2019-01-01 |
publisher | IEEE |
record_format | Article |
series | IEEE Access |
spelling | doaj.art-c7da34cf048f459dbfa035dd74e607d72022-12-21T22:23:09ZengIEEEIEEE Access2169-35362019-01-01717548717549810.1109/ACCESS.2019.29558348911435Fast and Efficient Non-Contact Ball Detector for Picking RobotsQi-Chao Mao0https://orcid.org/0000-0003-1986-4581Hong-Mei Sun1https://orcid.org/0000-0002-4830-3364Yan-Bo Liu2https://orcid.org/0000-0001-7374-675XRui-Sheng Jia3https://orcid.org/0000-0003-1612-4764College of Computer Science and Engineering, Shandong University of Science and Technology, Qingdao, ChinaCollege of Computer Science and Engineering, Shandong University of Science and Technology, Qingdao, ChinaCollege of Computer Science and Engineering, Shandong University of Science and Technology, Qingdao, ChinaCollege of Computer Science and Engineering, Shandong University of Science and Technology, Qingdao, ChinaObject detectors based on deep learning requires high-performance computing and large run-time memory footprint to maintain good detection performance. They bring high computation overhead and power consumption to on-board embedded devices of non-contact ball picking robot. Furthermore, it is difficult to deploy on the machine because the model size is so big. The accuracy of the existing simplified detectors deployed on embedded devices cannot meet the requirements of practical applications. Therefore, how to reduce floating point operations (FLOPs) and the size of model without notably sacrificing detection precision becomes an urgent problem to be solved. To solve this problem, a shuttle residual block which is more efficient network unit based on depthwise separable convolution was proposed. And we designed a non-contact ball object detector for picking robots, which is shallower than YOLOv3 and has narrower structure. We evaluate the proposed method on non-contact Ball dataset and compelling results are achieved by the proposed method. Compared with YOLOv3, the proposed method reduces FLOPs by 86.2%, declines parameter size by 89.5%. Overall, the proposed method achieves comparable detection accuracy than YOLOv3, and its speed is 2.2 times faster than YOLOv3.https://ieeexplore.ieee.org/document/8911435/Depthwise separable convolutionpicking robotsembedded devicedeep learning |
spellingShingle | Qi-Chao Mao Hong-Mei Sun Yan-Bo Liu Rui-Sheng Jia Fast and Efficient Non-Contact Ball Detector for Picking Robots IEEE Access Depthwise separable convolution picking robots embedded device deep learning |
title | Fast and Efficient Non-Contact Ball Detector for Picking Robots |
title_full | Fast and Efficient Non-Contact Ball Detector for Picking Robots |
title_fullStr | Fast and Efficient Non-Contact Ball Detector for Picking Robots |
title_full_unstemmed | Fast and Efficient Non-Contact Ball Detector for Picking Robots |
title_short | Fast and Efficient Non-Contact Ball Detector for Picking Robots |
title_sort | fast and efficient non contact ball detector for picking robots |
topic | Depthwise separable convolution picking robots embedded device deep learning |
url | https://ieeexplore.ieee.org/document/8911435/ |
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