基于角度敏感的空间注意力机制的轻量型旋转目标检测器

Light weight rotating object detector based on angle sensitive spatial attention mechanism

随着深度学习的快速发展,近年来越来越多的基于锚框的目标检测算法被应用于遥感图像上,然而提高算法精度的代价是牺牲了检测速度。因此,选择无锚框的目标检测网络框架,针对遥感场景的特点,提出了一种旋转框的遥感检测算法。根据旋转框与其外接矩形框的空间位置关系,提出了一种简单有效的旋转框表示方式。此外,设计了一种用于辅助检测旋转目标的角度敏感的空间注意力机制,通过引入角度信息提升模型对旋转目标的检测能力。提出的算法在公开遥感数据集DOTA上进行了实验,旋转框的目标检测网络的平均精度均值达到了68.5%,检测速度达到了每秒17.4帧图像。

With the rapid development of deep learning,more and more target detection algorithms based on anchor frame are applied to remote sensing images in recent years.However,the cost of improving the accuracy of the algorithm is to sacrifice the detection speed.Therefore,the target detection network framework of anchor free was chosen,and a remote sensing detection algorithm of rotating frame was proposed according to the characteristics of remote sensing scene.A simple and effective representation of rotating frame was proposed according to the spatial position relationship between rotating frame and its external rectangular frame.In addition,an angle sensitive attention mechanism was de-signed to assist the detection of rotating targets.By introducing angle information,the detection ability of the model for rotating targets was improved.The proposed algorithm was tested on the open remote sensing dataset DOTA.The mean average precision of target detection network of rotating frame is 68.5%and the detection speed is 17.4 frames per second.