PolarDet: a fast, more precise detector for rotated target in aerial images

Fast and precise object detection for high-resolution aerial images has been a challenging task over the years. Due to the sharp variations on object scale, rotation, and aspect ratio, most existing methods are inefficient and imprecise. In this paper, we represent the oriented objects by polar method in polar coordinate and propose PolarDet, a fast and accurate one-stage object detector based on that representation. Our detector introduces a sub-pixel center semantic structure to further improve classifying veracity. PolarDet achieves nearly all SOTA performance in aerial object detection tasks with faster inference speed. In detail, our approach obtains the SOTA results on DOTA, UCAS-AOD, HRSC with 76.64\% mAP, 97.01\% mAP, and 90.46\% mAP respectively. Most noticeably, our PolarDet gets the best performance and reaches the fastest speed(32fps) at the UCAS-AOD dataset.

[1]  Peter Reinartz,et al.  Towards Multi-class Object Detection in Unconstrained Remote Sensing Imagery , 2018, ACCV.

[2]  Hao Li,et al.  Objects detection for remote sensing images based on polar coordinates , 2020, ArXiv.

[3]  Kaiming He,et al.  Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks , 2015, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[4]  Jia Deng,et al.  Stacked Hourglass Networks for Human Pose Estimation , 2016, ECCV.

[5]  Yang Long,et al.  Learning RoI Transformer for Oriented Object Detection in Aerial Images , 2019, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

[6]  Yiping Yang,et al.  A High Resolution Optical Satellite Image Dataset for Ship Recognition and Some New Baselines , 2017, ICPRAM.

[7]  Hao Chen,et al.  FCOS: Fully Convolutional One-Stage Object Detection , 2019, 2019 IEEE/CVF International Conference on Computer Vision (ICCV).

[8]  Jian Sun,et al.  Deep Residual Learning for Image Recognition , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[9]  Kaiming He,et al.  PointRend: Image Segmentation As Rendering , 2020, 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

[10]  Enhua Wu,et al.  Squeeze-and-Excitation Networks , 2017, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[11]  Wenxian Yu,et al.  Toward Arbitrary-Oriented Ship Detection With Rotated Region Proposal and Discrimination Networks , 2018, IEEE Geoscience and Remote Sensing Letters.

[12]  Jiebo Luo,et al.  DOTA: A Large-Scale Dataset for Object Detection in Aerial Images , 2017, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.

[13]  Jian Guan,et al.  IENet: Interacting Embranchment One Stage Anchor Free Detector for Orientation Aerial Object Detection , 2019, ArXiv.

[14]  Gui-Song Xia,et al.  Rotation-Sensitive Regression for Oriented Scene Text Detection , 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.

[15]  Trevor Darrell,et al.  Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation , 2013, 2014 IEEE Conference on Computer Vision and Pattern Recognition.

[16]  Kaiming He,et al.  Feature Pyramid Networks for Object Detection , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[17]  Ali Farhadi,et al.  YOLO9000: Better, Faster, Stronger , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[18]  Wen Yang,et al.  Mask OBB: A Semantic Attention-Based Mask Oriented Bounding Box Representation for Multi-Category Object Detection in Aerial Images , 2019, Remote. Sens..

[19]  Jun Fu,et al.  Dual Attention Network for Scene Segmentation , 2018, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

[20]  Qi Tian,et al.  CenterNet: Keypoint Triplets for Object Detection , 2019, 2019 IEEE/CVF International Conference on Computer Vision (ICCV).

[21]  Xingyi Zhou,et al.  Bottom-Up Object Detection by Grouping Extreme and Center Points , 2019, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

[22]  Lei Liu,et al.  Learning a Rotation Invariant Detector with Rotatable Bounding Box , 2017, ArXiv.

[23]  Xiang Bai,et al.  TextBoxes++: A Single-Shot Oriented Scene Text Detector , 2018, IEEE Transactions on Image Processing.

[24]  Junchi Yan,et al.  R3Det: Refined Single-Stage Detector with Feature Refinement for Rotating Object , 2019, AAAI.

[25]  Jun Du,et al.  Adaptive Period Embedding for Representing Oriented Objects in Aerial Images , 2019, IEEE Transactions on Geoscience and Remote Sensing.

[26]  Mengyang Li,et al.  Single Shot Anchor Refinement Network for Oriented Object Detection in Optical Remote Sensing Imagery , 2019, IEEE Access.

[27]  Yiping Yang,et al.  Ship Rotated Bounding Box Space for Ship Extraction From High-Resolution Optical Satellite Images With Complex Backgrounds , 2016, IEEE Geoscience and Remote Sensing Letters.

[28]  Ali Farhadi,et al.  YOLOv3: An Incremental Improvement , 2018, ArXiv.

[29]  Xingyi Zhou,et al.  Objects as Points , 2019, ArXiv.

[30]  Yue Zhang,et al.  Rotation-aware and multi-scale convolutional neural network for object detection in remote sensing images , 2020 .

[31]  Wei Liu,et al.  SSD: Single Shot MultiBox Detector , 2015, ECCV.

[32]  Yue Zhang,et al.  SCRDet: Towards More Robust Detection for Small, Cluttered and Rotated Objects , 2018, 2019 IEEE/CVF International Conference on Computer Vision (ICCV).

[33]  Ali Farhadi,et al.  You Only Look Once: Unified, Real-Time Object Detection , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[34]  Yangyang Li,et al.  RADet: Refine Feature Pyramid Network and Multi-Layer Attention Network for Arbitrary-Oriented Object Detection of Remote Sensing Images , 2020, Remote. Sens..

[35]  Hao Li,et al.  Oriented Objects as pairs of Middle Lines , 2019, ArXiv.

[36]  Junchi Yan,et al.  SCRDet++: Detecting Small, Cluttered and Rotated Objects via Instance-Level Feature Denoising and Rotation Loss Smoothing , 2020, ArXiv.

[37]  Xue Yang,et al.  Learning Modulated Loss for Rotated Object Detection , 2019, ArXiv.

[38]  Tong Zhang,et al.  Feature-Attentioned Object Detection in Remote Sensing Imagery , 2019, 2019 IEEE International Conference on Image Processing (ICIP).

[39]  Andrew Zisserman,et al.  Spatial Transformer Networks , 2015, NIPS.

[40]  Jimmy Ba,et al.  Adam: A Method for Stochastic Optimization , 2014, ICLR.

[41]  Kai Chen,et al.  Gliding vertex on the horizontal bounding box for multi-oriented object detection , 2020, IEEE transactions on pattern analysis and machine intelligence.

[42]  Ross B. Girshick,et al.  Fast R-CNN , 2015, 1504.08083.

[43]  Wei Li,et al.  R2CNN: Rotational Region CNN for Orientation Robust Scene Text Detection , 2017, ArXiv.

[44]  Hei Law,et al.  CornerNet: Detecting Objects as Paired Keypoints , 2018, ECCV.

[45]  Ross B. Girshick,et al.  Focal Loss for Dense Object Detection , 2017, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[46]  Menglong Yan,et al.  Automatic Ship Detection in Remote Sensing Images from Google Earth of Complex Scenes Based on Multiscale Rotation Dense Feature Pyramid Networks , 2018, Remote. Sens..

[47]  Yi Li,et al.  R-FCN: Object Detection via Region-based Fully Convolutional Networks , 2016, NIPS.

[48]  Koen E. A. van de Sande,et al.  Selective Search for Object Recognition , 2013, International Journal of Computer Vision.

[49]  Zhuowen Tu,et al.  Aggregated Residual Transformations for Deep Neural Networks , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[50]  Xiangyang Xue,et al.  Arbitrary-Oriented Scene Text Detection via Rotation Proposals , 2017, IEEE Transactions on Multimedia.

[51]  Jian Sun,et al.  Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition , 2014, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[52]  Nuno Vasconcelos,et al.  Cascade R-CNN: Delving Into High Quality Object Detection , 2017, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.