An Efficient Feature Pyramid Network for Object Detection in Remote Sensing Imagery
Qingyun Fang, Zhang Lin, Wang Zhaokui
Abstract
Scale diversity, small target, and power limitation have made remote sensing imagery a challenging field in object detection on satellites. Aiming at the aspects of scale diversity and small target, this paper provides a novel feature pyramid network with Adaptive Residual Spatial Bi-Fusion (ARSF) as a solution. ARSF nets introduce a robust fusion of multi-scale semantic information and fine spatial details. A spatial feature fusion module designed in networks with ARSF adapts to object size variation by learning the most crucial feature maps. Comparing to the original feature pyramid network, a shorter critical path for information transmission is formed in our method. Experiments show that a validation instance of YOLOv3-ARSF can achieve a state-of-the-art performance of 85.8 mAP on the NWPU-VHR10 dataset. YOLOv3-ARSF only 3MB larger than YOLOv3 but far exceeds YOLOv3 by 2.3% mAP, which shows our ARSF is efficient. As for the last challenge, two lightweight versions, ARSF(lite) and ARSF(lite+) are also validated for future research of online object detection on satellites in aerospace engineering. Visualizations and details are provided for a more comprehensive understanding.