YOLO-UAV: Object Detection Method of Unmanned Aerial Vehicle Imagery Based on Efficient Multi-Scale Feature Fusion-Reference-Cited by-同舟云学术

YOLO-UAV: Object Detection Method of Unmanned Aerial Vehicle Imagery Based on Efficient Multi-Scale Feature Fusion

Published:2023 Issue: Volume:11 Page:126857-126878
ISSN:2169-3536
Container-title:IEEE Access
language:
Short-container-title:IEEE Access

Author:

Ma Chengji¹^ORCID,Fu Yanyun²^ORCID,Wang Deyong³^ORCID,Guo Rui³^ORCID,Zhao Xueyi³^ORCID,Fang Jian³^ORCID

Affiliation:

1. School of Information Science and Engineering (School of Cyberspace Security), Xinjiang University, Ürümqi, China

2. Beijing Academy of Science and Technology, Beijing, China

3. Key Laboratory of Big Data of Xinjiang Social Security Risk, Xinjiang Lianhaichuangzhi Information Technology Company Ltd., Ürümqi, China

Funder

National Natural Science Foundation of China: Intelligent Perception and Real-time Simulation and Deduction Technology for Urban Emergency Management Events

Key Laboratory of Big Data of Xinjiang Social Security Risk Prevention and Control

Autonomous Region High-Level Talent Introduction Project: Research on Unmanned Aerial Vehicle Information Reconnaissance and Big Data Analysis Technology for Public Safety

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

General Engineering,General Materials Science,General Computer Science,Electrical and Electronic Engineering

Link

http://xplorestaging.ieee.org/ielx7/6287639/10005208/10305077.pdf?arnumber=10305077

Reference55 articles.

1. ECascade-RCNN: Enhanced Cascade RCNN for Multi-scale Object Detection in UAV Images

2. An Improved Cascade R-CNN-Based Target Detection Algorithm for UAV Aerial Images

3. A Fast and Accurate Few-Shot Detector for Objects with Fewer Pixels in Drone Image

4. UAV Image Small Object Detection Based on Composite Backbone Network

5. Improved YOLOv4 for Aerial Object Detection

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