Mask R-CNN for Geospatial Object Detection

Mask R-CNN for Geospatial Object Detection

Автор: Dalal AL-Alimi, Yuxiang Shao, Ahamed Alalimi, Ahmed Abdu

Журнал: International Journal of Information Technology and Computer Science @ijitcs

Статья в выпуске: 5 Vol. 12, 2020 года.

Бесплатный доступ

Geospatial imaging technique has opened a door for researchers to implement multiple beneficial applications in many fields, including military investigation, disaster relief, and urban traffic control. As the resolution of geospatial images has increased in recent years, the detection of geospatial objects has attracted a lot of researchers. Mask R-CNN had been designed to identify an object outlines at the pixel level (instance segmentation), and for object detection in natural images. This study describes the Mask R-CNN model and uses it to detect objects in geospatial images. This experiment was prepared an existing dataset to be suitable with object segmentation, and it shows that Mask R-CNN also has the ability to be used in geospatial object detection and it introduces good results to extract the ten classes dataset of Seg-VHR-10.

Еще

Mask R-CNN, Faster R-CNN, RoIAlign, object detection, instance segmentation

Короткий адрес: https://sciup.org/15017467

IDR: 15017467   |   DOI: 10.5815/ijitcs.2020.05.05

Список литературы Mask R-CNN for Geospatial Object Detection

  • R. Girshick, J. Donahue, T. Darrell and J. Malik, "Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation," IEEE Conference on Computer Vision and Pattern Recognition, pp. 580-587, 2014.
  • D. AL-Alimi , Y. Shao, F. Ruyi , M. A. A. Al-qaness, M. Abd Elaziz and S. Kim, "Multi-Scale Geospatial Object Detection Based on Shallow-Deep Feature Extraction," Remote Sensing, vol. 11, no. 21, pp. 1-19, 29 10 2019.
  • Z.-Q. Zhao, P. Zheng, S.-T. Xu and X. Wu, "Object Detection With Deep Learning: A Review," IEEE Transactions on Neural Networks and Learning Systems, pp. 1-21, 2019.
  • Z. Zou, Z. Shi, Y. Guo and J. Ye, "Object Detection in 20 Years: A Survey," arXiv:1905.05055v1, pp. 1-40, 2019.
  • K. He, X. Zhang, S. Ren and J. Sun, "Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 37, no. 9, pp. 1904-1916, 2015.
  • R. Girshick, "Fast R-CNN," 2015 IEEE International Conference on Computer Vision (ICCV), pp. 1440-1448, 2015.
  • K. H. R. G. a. J. S. Shaoqing Ren, "Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 39, no. 6, pp. 1137-1149, 2017.
  • D. A. Wei Liu, D. Erhan, C. Szegedy, S. Reed, C.-Y. Fu and A. C. Berg, "SSD: Single Shot MultiBox Detector," Proc. European Conf. Computer Vision, 2016,, p. 21–37, 2016.
  • K. F. H. S. J. Y. Z. G. Xue Yang, "R2CNN++: Multi-Dimensional Attention Based Rotation Invariant Detector with Robust Anchor Strategy," arXiv, pp. 1-10, 2018.
  • W. Guo, W. Yang, H. Zhang and G. Hua, "Geospatial Object Detection in High Resolution Satellite Images Based on Multi-Scale Convolutional Neural Network," Remote Sens., vol. 10, no. 1, pp. 1-21, 2018.
  • K. Simonyan and A. Zisserman, "Very Deep Convolutional Networks for Large-Scale Image Recognition," arXiv, 2014.
  • T.-Y. Lin, P. Dollár, R. Girshick, K. He, B. Hariharan and S. Belongie, "Feature Pyramid Networks for Object Detection," arxiv, pp. 1-9, 2017.
  • K. He, G. Gkioxari, P. Doll´ar and R. Girshick, "Mask R-CNN," arXiv, pp. 1-12, 2018.
  • J. Long, S. Evan and T. Darrell , "Fully Convolutional Networksfor Semantic Segmentation," CVPR, 2015.
  • N. Dalal and B. Triggs, "Histograms of oriented gradients for human detection," 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05), vol. 1, pp. 886-893, 2005.
  • P. Viola and M. Jones, "Rapid object detection using a boosted cascade of simple features," IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 511-518, 2001.
  • C. Cortes and V. Vapnik, "Support vector machine," Machine learning, vol. 20, no. 3, p. 273–297, 1995.
  • F. F. Pedro, B. G. Ross, M. David and R. Deva, "Object Detection with Discriminatively Trained Part-Based Models," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 32, no. 9, 2010.
  • R. E. S. Yoav Freund, "A Decision-Theoretic Generalization of On-Line Learning and an Application to Boosting," Journal of Computer and System Sciences, vol. 55, no. 1, pp. 119-139, 1997.
  • J. Han, P. Zhou, D. Zhang, G. Cheng, L. Guo, Z. Liu, S. Bu and J. Wu, "Efficient, simultaneous detection of multi-class geospatial targets based on visual saliency modeling and discriminative learning of sparse coding," ISPRS Journal of Photogrammetry and Remote Sensing, vol. 89, pp. 37-48, 2014.
  • J. Han, D. Zhang, G. Cheng, L. Guo and J. Ren, "Object detection in optical remote sensing images based on weakly supervised learning and high-level feature learning," IEEE Transactions on Geoscience and Remote Sensing, vol. 53, no. 6, pp. 3325-3337, 2015.
  • G. Cheng, J. Han, P. Zhou and L. Guo, "Multi-class geospatial object detection and geographic image classification based on collection of part detectors," ISPRS Journal of Photogrammetry and Remote Sensing, pp. 119-132, 2014.
  • G. Cheng, P. Zhou and J. Han, "Learning Rotation-Invariant Convolutional Neural Networks for Object Detection in VHR Optical Remote Sensing Images," IEEE Transactions on Geoscience and Remote Sensing, vol. 54, no. 12, pp. 7405-7415, 2016.
  • F. Zhang, B. Du, L. Zhang and M. Xu, "Weakly Supervised Learning Based on Coupled Convolutional Neural Networks for Aircraft Detection," IEEE Transactions on Geoscience and Remote Sensing, vol. 54, no. 9, pp. 5553-5563, 2016.
  • F. Zhang, B. Du, L. Zhang and M. Xu, "Weakly Supervised Learning Based on Coupled Convolutional Neural Networks for Aircraft Detection," IEEE Transactions on Geoscience and Remote Sensing, vol. 54, no. 9, 2016.
  • Z. Deng, H. Sun, S. Zhou, J. Zhao, L. Lei and H. Zou, "Multi-scale object detection in remote sensing imagery with convolutional neural networks," ISPRS Journal of Photogrammetry and Remote Sensing, vol. 145, pp. 3-22, 2018.
  • D. A. Wei Liu, D. Erhan, C. Szegedy, S. Reed, C.-Y. Fu and A. C. Berg, "SSD:SingleShotMultiBoxDetector," Proc. European Conf. Computer Vision, 2016,, p. 21–37, 2016.
  • J. Redmon, S. Divvala, R. Girshick and A. Farhadi, "You Only Look Once: Unified, Real-Time Object Detection," IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 779-788, 2016.
  • J. Uijlings, K. v. d. Sande, T. Gevers and A. Smeulders, "Selective Search for Object Recognition," International Journal of Computer Vision, vol. 104, pp. 154-171, 2013.
  • J. Redmon and A. Farhadi, "YOLO9000: Better,Faster,Stronger," arXiv, pp. 1-9, 2016.
  • M. Bai and R. Urtasun , "Deep Watershed Transform for Instance Segmentation," CVPR, vol. 3, 2017.
  • A. Arnab and P. H. S. Torr, "Pixelwise Instance Segmentation with a Dynamically Instantiated Network," CVPR, vol. 3, 2017.
  • K. Z. X. R. S. He, "Deep residual learning for image recognition," Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, p. 770–778, 2016.
  • A. Dutta and A. Zisserman, "The VIA Annotation Software for Images, Audio and Video," in Proceedings of the 27th ACM International Conference on Multimedia, Nice, France, 2019.
  • R. Anantharaman, M. Velazquez and Y. Lee, "Utilizing Mask R-CNN for Detection and Segmentation of Oral Diseases," in 2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), Madrid, Spain, 2018.
  • J. H. Gong Cheng, "A survey on object detection in optical remote sensing images," ISPRS Journal of Photogrammetry and Remote Sensing, no. 177, pp. 11-28, 2016.
  • D. AL-Alimi , Y. Shao, F. Ruyi , M. A. A. Al-qaness, M. Abd Elaziz and S. Kim, "Multi-Scale Geospatial Object Detection Based on Shallow-Deep Feature Extraction," Remote Sensing, vol. 11, no. 21, pp. 1-19, 2019.
Еще
Статья научная
QR-код для установки приложения SciUp Наведите камеру и скачайте бесплатное приложение SciUp