An Anchor-free Detector Based on Residual Feature Enhancement Pyramid Network for UAV Vehicle Detection

Jianghuan Xie, Dianwei Wang, Jiaxing Guo, Pengfei Han, Jie Fang, Zhijie Xu

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Vehicle detection in Unmanned Aerial Vehicle (UAV) images is a challenging task because there are many small objects in UAV images, and the scale of objects varies greatly, which brings great difficulty to vehicle detection using existing algorithms. This paper proposes an anchor-free detector called Residual Feature Enhancement Pyramid Network (RFEPNet) for UAV vehicle detection. RFEPNet contains a Cross-Level Context Fusion Network (CLCFNet) and a Residual Feature Enhancement Module (RFEM) based on pyramid convolution. Specifically, CLCFNet utilizes the densely connected structure and Dual Attention Fusion Module (DAFM) to increase the sensitivity of high-resolution feature maps to small objects. Simultaneously, RFEM exploits pyramid convolution and residual connection structure to enhance the semantic information of the feature pyramid. In addition, the anchor-free head is used for classification and bounding box regression. The experimental results on the UAVDT dataset show that the proposed RFEPNet achieves state-of-the-art performance.

Original languageEnglish
Title of host publicationAIPR 2021 - 2021 4th International Conference on Artificial Intelligence and Pattern Recognition
PublisherAssociation for Computing Machinery (ACM)
Pages287-294
Number of pages8
ISBN (Electronic)9781450384087
DOIs
Publication statusPublished - 24 Sep 2021
Event4th International Conference on Artificial Intelligence and Pattern Recognition - Virtual, Online, China
Duration: 24 Sep 202126 Sep 2021
Conference number: 4
https://dl.acm.org/doi/proceedings/10.1145/3488933

Conference

Conference4th International Conference on Artificial Intelligence and Pattern Recognition
Abbreviated titleAIPR 2021
Country/TerritoryChina
CityVirtual, Online
Period24/09/2126/09/21
Internet address

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