YOLOv5 ship: An improved ship target detection algorithm study-Reference-Cited by-同舟云学术

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YOLOv5 ship: An improved ship target detection algorithm study

Published:2023-10-13 Issue: Volume: Page:106-116
ISSN:
Container-title:Proceedings of the 2023 7th International Conference on Advances in Artificial Intelligence
language:
Short-container-title:

Author:

Pu Wang¹^ORCID,Yang Shenhua¹^ORCID,Chen Guoquan¹^ORCID,Zheng Minjie¹^ORCID,Suo Yongfeng¹^ORCID,Wang Tao²^ORCID

Affiliation:

1. Navigation College, Jimei University, China

2. School of Shipping and Ship Engineering, Chongqing Jiaotong University, China

Funder

National Natural Science Foundation of China

Naural Science Project of Fujian Province

Key Projects of the National Key R & D Program

Fuzhou-Xiamen-Quanzhou Independent Innovation Region Cooperated Special Foundation

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3633598.3633616

Reference35 articles.

1. Enhanced YOLO v3 Tiny Network for Real-Time Ship Detection From Visual Image

2. An Improved Method for Ship Target Detection Based on YOLOv4

3. Ship Target Detection Algorithm Based on Improved Faster R-CNN

4. Attention Mask R-CNN for Ship Detection and Segmentation From Remote Sensing Images

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