Vector encoded bounding box regression for detecting remote-sensing objects with anchor-free methods

ABSTRACT We propose a novel convolutional neural network architecture used for detecting objects in high-resolution remote-sensing images. Different from previous detectors, our method is totally anchor-free. In the architecture, we design a new regression method by encoding the bounding boxes into vectors and bring direction information into the network. We also analysed the detection head and proposed the Faster activated detector heads module to accelerate the convergence speed. Experiments were carried out on two public remote-sensing image datasets. Comparing with previous methods, our work shows the most favourable result in the detecting accuracy with no extra trainable parameters added.

[1]  Sergey Ioffe,et al.  Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift , 2015, ICML.

[2]  Li Fei-Fei,et al.  ImageNet: A large-scale hierarchical image database , 2009, CVPR.

[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]  Stephen Lin,et al.  RepPoints: Point Set Representation for Object Detection , 2019, 2019 IEEE/CVF International Conference on Computer Vision (ICCV).

[5]  Liangpei Zhang,et al.  An Efficient and Robust Integrated Geospatial Object Detection Framework for High Spatial Resolution Remote Sensing Imagery , 2017, Remote. Sens..

[6]  Kaiming He,et al.  Mask R-CNN , 2017, 2017 IEEE International Conference on Computer Vision (ICCV).

[7]  Pietro Perona,et al.  Microsoft COCO: Common Objects in Context , 2014, ECCV.

[8]  Xin Pan,et al.  A central-point-enhanced convolutional neural network for high-resolution remote-sensing image classification , 2017 .

[9]  Junwei Han,et al.  Multi-class geospatial object detection and geographic image classification based on collection of part detectors , 2014 .

[10]  Yi Yang,et al.  DenseBox: Unifying Landmark Localization with End to End Object Detection , 2015, ArXiv.

[11]  Paul A. Viola,et al.  Robust Real-Time Face Detection , 2001, International Journal of Computer Vision.

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

[13]  Ping Zhong,et al.  A Multiple Conditional Random Fields Ensemble Model for Urban Area Detection in Remote Sensing Optical Images , 2007, IEEE Transactions on Geoscience and Remote Sensing.

[14]  Shu Liu,et al.  Path Aggregation Network for Instance Segmentation , 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.

[15]  Bill Triggs,et al.  Histograms of oriented gradients for human detection , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

[16]  Junwei Han,et al.  Learning Rotation-Invariant Convolutional Neural Networks for Object Detection in VHR Optical Remote Sensing Images , 2016, IEEE Transactions on Geoscience and Remote Sensing.

[17]  David A. McAllester,et al.  Object Detection with Discriminatively Trained Part Based Models , 2010, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[18]  Zhenwei Shi,et al.  Airplane detection based on rotation invariant and sparse coding in remote sensing images , 2014 .

[19]  Xian Sun,et al.  Object Detection in High-Resolution Remote Sensing Images Using Rotation Invariant Parts Based Model , 2014, IEEE Geoscience and Remote Sensing Letters.

[20]  Jun Wu,et al.  A Hierarchical Oil Tank Detector With Deep Surrounding Features for High-Resolution Optical Satellite Imagery , 2015, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

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

[22]  Ke Li,et al.  Rotation-Insensitive and Context-Augmented Object Detection in Remote Sensing Images , 2018, IEEE Transactions on Geoscience and Remote Sensing.

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

[24]  Serkan Ozturk,et al.  A subclass supported convolutional neural network for object detection and localization in remote-sensing images , 2019, International Journal of Remote Sensing.

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

[26]  Marios Savvides,et al.  Feature Selective Anchor-Free Module for Single-Shot Object Detection , 2019, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

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

[28]  Yanfei Zhong,et al.  Multi-class geospatial object detection based on a position-sensitive balancing framework for high spatial resolution remote sensing imagery , 2018 .

[29]  Bo Du,et al.  Weakly Supervised Learning Based on Coupled Convolutional Neural Networks for Aircraft Detection , 2016, IEEE Transactions on Geoscience and Remote Sensing.

[30]  Hei Law,et al.  CornerNet: Detecting Objects as Paired Keypoints , 2018, International Journal of Computer Vision.

[31]  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..

[32]  Lei Guo,et al.  Object Detection in Optical Remote Sensing Images Based on Weakly Supervised Learning and High-Level Feature Learning , 2015, IEEE Transactions on Geoscience and Remote Sensing.

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

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

[35]  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).

[36]  Junwei Han,et al.  A Survey on Object Detection in Optical Remote Sensing Images , 2016, ArXiv.

[37]  Luc Van Gool,et al.  The Pascal Visual Object Classes (VOC) Challenge , 2010, International Journal of Computer Vision.

[38]  Xiwen Yao,et al.  Cross-Scale Feature Fusion for Object Detection in Optical Remote Sensing Images , 2021, IEEE Geoscience and Remote Sensing Letters.

[39]  Xinyu Liu,et al.  TanhExp: A Smooth Activation Function with High Convergence Speed for Lightweight Neural Networks , 2020, IET Comput. Vis..

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

[41]  Gang Wan,et al.  Object Detection in Optical Remote Sensing Images: A Survey and A New Benchmark , 2020, ISPRS Journal of Photogrammetry and Remote Sensing.

[42]  Wei Guo,et al.  Geospatial Object Detection in High Resolution Satellite Images Based on Multi-Scale Convolutional Neural Network , 2018, Remote. Sens..

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

[44]  Gong Cheng,et al.  Object Detection in Remote Sensing Images Based on Improved Bounding Box Regression and Multi-Level Features Fusion , 2020, Remote. Sens..

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

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

[47]  Zhaohui Zheng,et al.  Distance-IoU Loss: Faster and Better Learning for Bounding Box Regression , 2019, AAAI.

[48]  Yuning Jiang,et al.  UnitBox: An Advanced Object Detection Network , 2016, ACM Multimedia.

[49]  Silvio Savarese,et al.  Generalized Intersection Over Union: A Metric and a Loss for Bounding Box Regression , 2019, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

[50]  Dong Xu,et al.  Learning Rotation-Invariant and Fisher Discriminative Convolutional Neural Networks for Object Detection , 2019, IEEE Transactions on Image Processing.

[51]  Geoffrey E. Hinton,et al.  Rectified Linear Units Improve Restricted Boltzmann Machines , 2010, ICML.