AD-RCNN: Adaptive Dynamic Neural Network for Small Object Detection-Reference-Cited by-同舟云学术

AD-RCNN: Adaptive Dynamic Neural Network for Small Object Detection

Published:2023-03-01 Issue:5 Volume:10 Page:4226-4238
ISSN:2327-4662
Container-title:IEEE Internet of Things Journal
language:
Short-container-title:IEEE Internet Things J.

Author:

Ou Zhonghong¹^ORCID,Wang Zhaofengnian¹^ORCID,Xiao Fenrui¹^ORCID,Xiong Baiqiao¹^ORCID,Zhang Hongxing¹,Song Meina¹^ORCID,Zheng Yan²^ORCID,Hui Pan³^ORCID

Affiliation:

1. School of Computer Science (National Pilot Software School), Beijing University of Posts and Telecommunications, Beijing, China

2. School of Cyber Engineering, Xidian University, Xi’an, China

3. Department of Computer Science and Engineering, Hong Kong University of Science and Technology, Hong Kong, China

Funder

National Key Research and Development Program of China

National Natural Science Foundation of China

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Computer Networks and Communications,Computer Science Applications,Hardware and Architecture,Information Systems,Signal Processing

Link

http://xplorestaging.ieee.org/ielx7/6488907/10049212/09924201.pdf?arnumber=9924201

Reference29 articles.

1. Fast R-CNN

2. FoveaBox: Beyound Anchor-Based Object Detection

3. Region Proposal by Guided Anchoring

4. Mask R-CNN

5. FCOS: Fully Convolutional One-Stage Object Detection

Cited by 10 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. Concerning Imbalance and Bounding Box Loss to Detect Small Targets in Remote Sensing;IEEE Sensors Journal;2024-09-01

2. Dense object detection methods in RAW UAV imagery based on YOLOv8;Scientific Reports;2024-08-04

3. Toward Effective Traffic Sign Detection via Two-Stage Fusion Neural Networks;IEEE Transactions on Intelligent Transportation Systems;2024-08

4. A Feature-Enhanced and Adaptive Routing Framework for Fish School Detection on AUVs for Degraded Underwater Imaging Environments;IEEE Internet of Things Journal;2024-05-15

5. Efficient Parcel Damage Detection via Faster R-CNN: A Deep Learning Approach for Logistical Parcels’ Automated Inspection;Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering;2024