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International Journal of Advanced Robotic Systems
Article . 2020 . Peer-reviewed
License: CC BY
Data sources: Crossref
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https://dx.doi.org/10.60692/sc...
Other literature type . 2020
Data sources: Datacite
https://dx.doi.org/10.60692/39...
Other literature type . 2020
Data sources: Datacite
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A modified YOLOv3 detection method for vision-based water surface garbage capture robot

طريقة معدلة للكشف عن YOLOv3 لروبوت التقاط القمامة على سطح الماء القائم على الرؤية
Authors: Xiali Li; Manjun Tian; Shihan Kong; Licheng Wu; Junzhi Yu;

A modified YOLOv3 detection method for vision-based water surface garbage capture robot

Abstract

To tackle the water surface pollution problem, a vision-based water surface garbage capture robot has been developed in our lab. In this article, we present a modified you only look once v3-based garbage detection method, allowing real-time and high-precision object detection in dynamic aquatic environments. More specifically, to improve the real-time detection performance, the detection scales of you only look once v3 are simplified from 3 to 2. Besides, to guarantee the accuracy of detection, the anchor boxes of our training data set are reclustered for replacing some of the original you only look once v3 prior anchor boxes that are not appropriate to our data set. By virtue of the proposed detection method, the capture robot has the capability of cleaning floating garbage in the field. Experimental results demonstrate that both detection speed and accuracy of the modified you only look once v3 are better than those of other object detection algorithms. The obtained results provide valuable insight into the high-speed detection and grasping of dynamic objects in complex aquatic environments autonomously and intelligently.

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Keywords

Visual Odometry, Artificial intelligence, TK7800-8360, Data set, Object detection, Robot, Robot Navigation, Aerospace Engineering, FOS: Mechanical engineering, Set (abstract data type), Simultaneous Localization and Mapping, Garbage, Pattern recognition (psychology), Sampling-Based Motion Planning Algorithms, Engineering, Field (mathematics), FOS: Mathematics, Real-time Water Quality Monitoring and Aquaculture Management, Water Science and Technology, Pure mathematics, QA75.5-76.95, Computer science, Programming language, Electronic computers. Computer science, Environmental Science, Physical Sciences, Computer Science, Computer vision, Computer Vision and Pattern Recognition, Water Quality Monitoring, Electronics, Mathematics

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    popularity
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    Top 1%
    influence
    This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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    impulse
    This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
80
Top 1%
Top 1%
Top 1%
gold