TY - GEN
T1 - Pattern Analysis of Deformable Convolution on Retinanet with Semantic Filter Mechanism for Object Detection
AU - Zhu, Shengyu
AU - Zhang, Junping
AU - Guo, Qingle
AU - Zhong, Chongxiao
N1 - Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - Object detection for high resolution images has been an important cornerstone in remote sensing interpretation. Though considerable success has been made, there still exists issues for hierarchical semantic representation and integration, which limit the performance of existing methods. Therefore, we first analyze different integration patterns of deformable convolution on RetinaNet comprehensively to enhance the adaptability for the geometrical variations elegantly, which may provide the instructions of the future backbone designs for remote sensing images. Second, a feature pyramid network with filter mechanism (F-FPN) is proposed to strengthen the interaction of hierarchical semantics in feature reflow. Experiments on a challenging public dataset DIOR indicate the great performance.
AB - Object detection for high resolution images has been an important cornerstone in remote sensing interpretation. Though considerable success has been made, there still exists issues for hierarchical semantic representation and integration, which limit the performance of existing methods. Therefore, we first analyze different integration patterns of deformable convolution on RetinaNet comprehensively to enhance the adaptability for the geometrical variations elegantly, which may provide the instructions of the future backbone designs for remote sensing images. Second, a feature pyramid network with filter mechanism (F-FPN) is proposed to strengthen the interaction of hierarchical semantics in feature reflow. Experiments on a challenging public dataset DIOR indicate the great performance.
KW - Object detection
KW - deformable convolution pattern analysis
KW - high-resolution remote sensing images
KW - semantic filter mechanism
UR - https://www.scopus.com/pages/publications/85140354495
U2 - 10.1109/IGARSS46834.2022.9884412
DO - 10.1109/IGARSS46834.2022.9884412
M3 - 会议稿件
AN - SCOPUS:85140354495
T3 - International Geoscience and Remote Sensing Symposium (IGARSS)
SP - 3071
EP - 3074
BT - IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2022 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2022
Y2 - 17 July 2022 through 22 July 2022
ER -