As the rapid development of depth learning, object detection in aviatic remote sensing images has become increasingly popular in recent years. Most of the current Anchor Free detectors based on key point detection sampling directly regression and classification features, with the design of object loss function based on the horizontal bounding box. It is more challenging for complex and diverse aviatic remote sensing object. In this paper, we propose an Anchor Free aviatic remote sensing object detector (BWP-Det) to detect rotating and multi-scale object. Specifically, we design a interactive double-branch(IDB) up-sampling network, in which one branch gradually up-sampling is used for the prediction of Heatmap, and the other branch is used for the regression of boundary box parameters. We improve a weighted multi-scale convolution (WmConv) in order to highlight the difference between foreground and background. We extracted Pixel level attention features from the middle layer to guide the two branches to pay attention to effective object information in the sampling process. Finally, referring to the calculation idea of horizontal IoU, we design a rotating IoU based on the split polar coordinate plane, namely JIoU, which is expressed as the intersection ratio following discretization of the inner ellipse of the rotating bounding box, to solve the correlation between angle and side length in the regression process of the rotating bounding box. Ultimately, BWP-Det, our experiments on DOTA, UCAS-AOD and NWPU VHR-10 datasets show, achieves advanced performance with simpler models and fewer regression parameters.

翻译：随着深度学习的快速发展，航空遥感图像中的目标检测近年来日益普及。当前多数基于关键点检测的无锚点检测器直接对回归与分类特征进行采样，其目标损失函数设计基于水平边界框。这对于复杂多样的航空遥感目标而言更具挑战性。本文提出一种无锚点航空遥感目标检测器（BWP-Det），用于检测旋转和多尺度目标。具体而言，我们设计了一种交互式双分支上采样网络（IDB），其中一个分支逐步上采样用于热力图预测，另一分支用于边界框参数回归。为突出前景与背景差异，我们改进了加权多尺度卷积（WmConv）。从中间层提取像素级注意力特征，引导两个分支在采样过程中关注有效目标信息。最后，参考水平IoU的计算思想，基于分割极坐标平面设计了旋转IoU（记为JIoU），其表示为旋转边界框内椭圆离散化后的交并比，以解决旋转边界框回归过程中角度与边长之间的相关性。最终，在DOTA、UCAS-AOD和NWPU VHR-10数据集上的实验表明，BWP-Det以更简单的模型和更少的回归参数实现了先进性能。