Abstract: Oriented object detection in remote sensing images is more challenging due to the issues of complex background,dense distribution and with arbitrary direction,large-scale variation,high aspect-ratio of objects.To address these issues,this paper proposes a framework for oriented object detection in remote sensing images based on multi-scale perception enhancemen.Firstly,a multi-scale perceptual enhancement module is proposed in the feature extraction stage,which employs different convolutional blocks for extracting features for different levels of feature maps to ensure that the low-level feature maps retain enough detail information and the high-level feature maps extract enough semantic information.So that the extracted multilevel feature maps have the ability of adaptive feature learning for different scales.Meanwhile,an adaptive channel attention module is used to adaptively learn the channel weights to mitigate the effects of the complex background.Secondly,a size-sensitive rotated Itersection over Union(IoU) loss is proposed to supervise the network to learn the size information of the target and increase the sensitivity to high aspect ratio targets by adding the loss terms of objects' aspect ratio and area in the loss.The proposed method achieves 77.64%,98.32%,and 66.14% mAP on the publicly available remote sensing image datasets DOTA,HRSC2016,and DIOR-R,respectively.The detection accuracies of the proposed framework outperform existing state-of-the-art remote sensing image detection networks.

Key words: Remote sensing image, Oriented object detection, Multi-scale perceptual enhancement, Adaptive feature learning, Rotated IoU loss