Underwater targets detection and classification in complex scenes based on an improved YOLOv3 algorithm-Reference-Cited by-同舟云学术

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Underwater targets detection and classification in complex scenes based on an improved YOLOv3 algorithm

Published:2020-07-27 Issue:04 Volume:29 Page:1
ISSN:1017-9909
Container-title:Journal of Electronic Imaging
language:
Short-container-title:J. Electron. Imag.

Author:

Shi Tingchao¹,Liu Mingyong¹,Niu Yun¹,Yang Yang¹,Huang Yuxuan¹

Affiliation:

1. Northwestern Polytechnical University, School of Marine Science and Engineering, Science and Technol

Funder

National Natural Science Foundation of China

Publisher

SPIE-Intl Soc Optical Eng

Subject

Electrical and Electronic Engineering,Computer Science Applications,Atomic and Molecular Physics, and Optics

Cited by 15 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Real-Time Cross-View Image Matching and Camera Pose Determination for Unmanned Aerial Vehicles;Photogrammetric Engineering & Remote Sensing;2024-06-01

2. An Improved YOLO Network for Insulator and Insulator Defect Detection in UAV Images;Photogrammetric Engineering & Remote Sensing;2024-06-01

3. MFDNN: multi-scale feature-weighted dual-neck network for underwater object detection;Journal of Electronic Imaging;2024-04-22

4. Development of an application for recognizing mathematical formulas using convolutional neural networks;2024 5th International Conference on Innovative Trends in Information Technology (ICITIIT);2024-03-15

5. SO-YOLOv5: Small object recognition algorithm for sea cucumber in complex seabed environment;Fisheries Research;2023-08

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