关键词: 遥感图像, 目标检测, 舰船检测, 旋转卷积, 深度学习

Abstract: The orientation diversity of target in remote sensing images caused by the bird-eye view affects the rotation invariance of ship detection with large aspect ratio. Aiming at this problem, this paper proposes an incline bounding box detection model based on improved YOLOv3. The incline bounding box regression is implemented by introducing angle prediction. A rotation convolution ensemble module is proposed to increase the awareness for Deep Convolutional Neural Networks（DCNN） feature map to angle change by rotation convolution and rotation activation. The bounding box angle prediction is modeled as a two-step classification problem from coarse to fine. The error of angle classification punishment is introduced into the multi-task loss function of the model, so that the model can learn the angular offset of the object. Experiments on ship dataset show that the proposed model increases the Average Precision（AP） of ship detection in the test dataset by 12.7% compared with the classic YOLOv3 model, while maintaining the advantage of single-stage model in detection speed.

Key words: remote sensing images, object detection, ship detection, rotation convolution, deep learning