Towards More Efficient EfficientDets and Real-Time Marine Debris Detection-Reference-Cited by-同舟云学术

Towards More Efficient EfficientDets and Real-Time Marine Debris Detection

Published:2023-04 Issue:4 Volume:8 Page:2134-2141
ISSN:2377-3766
Container-title:IEEE Robotics and Automation Letters
language:
Short-container-title:IEEE Robot. Autom. Lett.

Author:

Zocco Federico¹^ORCID,Lin Tzu-Chieh²,Huang Ching-I²^ORCID,Wang Hsueh-Cheng²^ORCID,Khyam Mohammad Omar³^ORCID,Van Mien¹^ORCID

Affiliation:

1. Centre for Intelligent Autonomous Manufacturing Systems, School of Electronics, Electrical Engineering and Computer Science, Queen's University Belfast, Northern Ireland, U.K.

2. Department of Electrical and Computer Engineering, National Chiao Tung University (NCTU), Hsinchu City, Taiwan

3. School of Engineering and Technology, Central Queensland University, Norman Gardens, QLD, Australia

Funder

Natural Environment Research Council, United Kingdom

Taiwan National Science and Technology Council

Qualcomm through the Taiwan University Research Collaboration Project

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Artificial Intelligence,Control and Optimization,Computer Science Applications,Computer Vision and Pattern Recognition,Mechanical Engineering,Human-Computer Interaction,Biomedical Engineering,Control and Systems Engineering

Link

http://xplorestaging.ieee.org/ielx7/7083369/10049284/10044917.pdf?arnumber=10044917

Reference28 articles.

1. SSD: Single Shot MultiBox Detector

2. TrashCan: A semantically-segmented dataset towards visual detection of marine debris;hong,2020

3. Focal Loss for Dense Object Detection

4. YOLO9000: Better, Faster, Stronger

5. Robotic Detection of Marine Litter Using Deep Visual Detection Models

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