Unified Partial Configuration Model Framework for Fast Partially Occluded Object Detection in High-Resolution Remote Sensing Images

Partially occluded object detection (POOD) has been an important task for both civil and military applications that use high-resolution remote sensing images (HR-RSIs). This topic is very challenging due to the limited object evidence for detection. Recent partial configuration model (PCM) based methods deal with occlusion yet suffer from the problems of massive manual annotation, separate parameter learning, and low training and detection efficiency. To tackle this, a unified PCM framework (UniPCM) is proposed in this paper. The proposed UniPCM adopts a part sharing mechanism which directly shares the root and part filters of a deformable part-based model (DPM) among different partial configurations. It largely reduces the convolution overhead during both training and detection. In UniPCM, a novel DPM deformation deviation method is proposed for spatial interrelationship estimation of PCM, and a unified weights learning method is presented to simultaneously obtain the weights of elements within each partial configuration and the weights between partial configurations. Experiments on three HR-RSI datasets show that the proposed UniPCM method achieves a much higher training and detection efficiency for POOD compared with state-of-the-art PCM-based methods, while maintaining a comparable detection accuracy. UniPCM obtains a training speedup of maximal 10× and 2.5× for airplane and ship, and a detection speedup of maximal 7.2×, 4.1× and 2.5× on three test sets, respectively.

[1]  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.

[2]  Gongjian Wen,et al.  Occluded Object Detection in High-Resolution Remote Sensing Images Using Partial Configuration Object Model , 2017, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[3]  Zhiguo Jiang,et al.  Airport Detection Using End-to-End Convolutional Neural Network with Hard Example Mining , 2017, Remote. Sens..

[4]  Xiaogang Wang,et al.  A discriminative deep model for pedestrian detection with occlusion handling , 2012, 2012 IEEE Conference on Computer Vision and Pattern Recognition.

[5]  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.

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

[7]  Yi Li,et al.  Deformable Convolutional Networks , 2017, 2017 IEEE International Conference on Computer Vision (ICCV).

[8]  Jing Chen,et al.  A Decision Mixture Model-Based Method for Inshore Ship Detection Using High-Resolution Remote Sensing Images , 2017, Sensors.

[9]  Seiichi Mita,et al.  Occlusion handling using discriminative model of trained part templates and conditional random field , 2013, 2013 IEEE Intelligent Vehicles Symposium (IV).

[10]  D. T. Lee,et al.  Two algorithms for constructing a Delaunay triangulation , 1980, International Journal of Computer & Information Sciences.

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

[12]  Chunhong Pan,et al.  Feature Extraction by Rotation-Invariant Matrix Representation for Object Detection in Aerial Image , 2017, IEEE Geoscience and Remote Sensing Letters.

[13]  Xian Sun,et al.  A generic discriminative part-based model for geospatial object detection in optical remote sensing images , 2015 .

[14]  Huanxin Zou,et al.  Toward Fast and Accurate Vehicle Detection in Aerial Images Using Coupled Region-Based Convolutional Neural Networks , 2017, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[15]  Igor Sevo,et al.  Convolutional Neural Network Based Automatic Object Detection on Aerial Images , 2016, IEEE Geoscience and Remote Sensing Letters.

[16]  Shiming Xiang,et al.  Vehicle Detection in Satellite Images by Hybrid Deep Convolutional Neural Networks , 2014, IEEE Geoscience and Remote Sensing Letters.

[17]  D. West.  Introduction to Graph Theory , 1995 .

[18]  Jungho Im,et al.  Support vector machines in remote sensing: A review , 2011 .

[19]  Feng Li,et al.  An Effective Method Based on ACF for Aircraft Detection in Remote Sensing Images , 2017, IEEE Geoscience and Remote Sensing Letters.

[20]  Lin Lei,et al.  Arbitrary-Oriented Vehicle Detection in Aerial Imagery with Single Convolutional Neural Networks , 2017, Remote. Sens..

[21]  Menglong Yan,et al.  Vehicle detection in remote sensing images using denoizing-based convolutional neural networks , 2017 .

[22]  Junwei Han,et al.  Object detection in remote sensing imagery using a discriminatively trained mixture model , 2013 .

[23]  Fei-Fei Li,et al.  ImageNet: A large-scale hierarchical image database , 2009, 2009 IEEE Conference on Computer Vision and Pattern Recognition.

[24]  Hong Huo,et al.  Rotation-Invariant Object Detection of Remotely Sensed Images Based on Texton Forest and Hough Voting , 2012, IEEE Transactions on Geoscience and Remote Sensing.

[25]  Geoffrey E. Hinton,et al.  ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.

[26]  Xuan Wang,et al.  Ship Detection in Optical Remote Sensing Images Based on Wavelet Transform and Multi-Level False Alarm Identification , 2017, Remote. Sens..

[27]  Konrad Schindler,et al.  Explicit Occlusion Modeling for 3D Object Class Representations , 2013, 2013 IEEE Conference on Computer Vision and Pattern Recognition.

[28]  Rogério Schmidt Feris,et al.  A Unified Multi-scale Deep Convolutional Neural Network for Fast Object Detection , 2016, ECCV.

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

[30]  Qing Liu,et al.  Accurate Object Localization in Remote Sensing Images Based on Convolutional Neural Networks , 2017, IEEE Transactions on Geoscience and Remote Sensing.

[31]  Xiaobin Li,et al.  Object Detection Using Convolutional Neural Networks in a Coarse-to-Fine Manner , 2017, IEEE Geoscience and Remote Sensing Letters.

[32]  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.

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

[34]  Gongjian Wen,et al.  Automatic and Fast PCM Generation for Occluded Object Detection in High-Resolution Remote Sensing Images , 2017, IEEE Geoscience and Remote Sensing Letters.