TY - GEN
T1 - An Anchor-free Detector Based on Residual Feature Enhancement Pyramid Network for UAV Vehicle Detection
AU - Xie, Jianghuan
AU - Wang, Dianwei
AU - Guo, Jiaxing
AU - Han, Pengfei
AU - Fang, Jie
AU - Xu, Zhijie
N1 - Publisher Copyright:
© 2021 ACM.
PY - 2021/9/24
Y1 - 2021/9/24
N2 - 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.
AB - 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.
KW - Anchor-free
KW - Cross-Level Context Fusion Network
KW - Residual Feature Enhancement
KW - UAV vehicle detection
UR - http://www.scopus.com/inward/record.url?scp=85125851036&partnerID=8YFLogxK
U2 - 10.1145/3488933.3488936
DO - 10.1145/3488933.3488936
M3 - Conference Proceeding
AN - SCOPUS:85125851036
T3 - ACM International Conference Proceeding Series
SP - 287
EP - 294
BT - AIPR 2021 - 2021 4th International Conference on Artificial Intelligence and Pattern Recognition
PB - Association for Computing Machinery
T2 - 4th International Conference on Artificial Intelligence and Pattern Recognition, AIPR 2021
Y2 - 17 September 2021 through 19 September 2021
ER -