Improved SSD Algorithm and Its Performance Analysis of Small Target Detection in Remote Sensing Images

Junqiang Wang; Jiansheng Li; Xuewen Zhou; Xu Zhang

doi:10.3788/AOS201939.0628005

[1] Wang G X, Huang X T, Zhou Z M. UWB SAR change detection of target in foliage based on local statistic distribution change analysis[J]. Journal of Electronics & Information Technology, 33, 49-54(2011).

Wang G X, Huang X T, Zhou Z M. UWB SAR change detection of target in foliage based on local statistic distribution change analysis[J]. Journal of Electronics & Information Technology, 33, 49-54(2011).

[2] Wu W. Research on knowledge-based target recognition and tracking techniques[D]. Harbin: Harbin Institute of Technology, 10-28(2007).

Wu W. Research on knowledge-based target recognition and tracking techniques[D]. Harbin: Harbin Institute of Technology, 10-28(2007).

[3] Cao J Z, Song A G. Research on the texture image segmentation method based on Markov random field[J]. Chinese Journal of Scientific Instrument, 36, 776-786(2015).

Cao J Z, Song A G. Research on the texture image segmentation method based on Markov random field[J]. Chinese Journal of Scientific Instrument, 36, 776-786(2015).

[4] Russakovsky O, Deng J, Su H et al. ImageNet large scale visual recognition challenge[J]. International Journal of Computer Vision, 115, 211-252(2015). http://dl.acm.org/citation.cfm?id=2846559"

Russakovsky O, Deng J, Su H et al. ImageNet large scale visual recognition challenge[J]. International Journal of Computer Vision, 115, 211-252(2015). http://dl.acm.org/citation.cfm?id=2846559"

[5] Girshick R. Fast R-CNN. [C]∥2015 IEEE International Conference on Computer Vision (ICCV), December 7-13, 2015, Santiago, Chile. New York: IEEE, 1440-1448(2015).

Girshick R. Fast R-CNN. [C]∥2015 IEEE International Conference on Computer Vision (ICCV), December 7-13, 2015, Santiago, Chile. New York: IEEE, 1440-1448(2015).

[6] Ren S Q, He K M, Girshick R et al. Faster R-CNN: towards real-time object detection with region proposal networks[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 39, 1137-1149(2017). http://www.ncbi.nlm.nih.gov/pubmed/27295650

Ren S Q, He K M, Girshick R et al. Faster R-CNN: towards real-time object detection with region proposal networks[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 39, 1137-1149(2017). http://www.ncbi.nlm.nih.gov/pubmed/27295650

[7] Dai J, Li Y, He K et al. R-FCN: object detection via region-based fully convolutional networks. [C]∥NIPS'16 Proceedings of the 30th International Conference on Neural Information Processing Systems, December 5-10, 2016, Barcelona, Spain. USA: Curran Associates Inc., 379-387(2016).

Dai J, Li Y, He K et al. R-FCN: object detection via region-based fully convolutional networks. [C]∥NIPS'16 Proceedings of the 30th International Conference on Neural Information Processing Systems, December 5-10, 2016, Barcelona, Spain. USA: Curran Associates Inc., 379-387(2016).

[8] Redmon J, Divvala S, Girshick R et al. You only look once: unified, real-time object detection. [C]∥2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 27-30, 2016, Las Vegas, NV, USA. New York: IEEE, 779-788(2016).

Redmon J, Divvala S, Girshick R et al. You only look once: unified, real-time object detection. [C]∥2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 27-30, 2016, Las Vegas, NV, USA. New York: IEEE, 779-788(2016).

[9] Liu W, Anguelov D, Erhan D et al. SSD: single shot multibox detector[M]. ∥Leibe B, Matas J, Sebe N, et al. Computer Vision-ECCV 2016. Cham: Springer, 9905, 21-37(2016).

Liu W, Anguelov D, Erhan D et al. SSD: single shot multibox detector[M]. ∥Leibe B, Matas J, Sebe N, et al. Computer Vision-ECCV 2016. Cham: Springer, 9905, 21-37(2016).

[10] Lin T Y, Goyal P, Girshick R et al. Focal loss for dense object detection[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2858826(2018). http://ieeexplore.ieee.org/document/8417976/

Lin T Y, Goyal P, Girshick R et al. Focal loss for dense object detection[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2858826(2018). http://ieeexplore.ieee.org/document/8417976/

[11] Huang J, Rathod V, Sun C et al. Speed/accuracy trade-offs for modern convolutional object detectors. [C]∥2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), July 21-26, 2017, Honolulu, HI, USA. New York: IEEE, 3296-3297(2017).

Huang J, Rathod V, Sun C et al. Speed/accuracy trade-offs for modern convolutional object detectors. [C]∥2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), July 21-26, 2017, Honolulu, HI, USA. New York: IEEE, 3296-3297(2017).

[12] Lin T Y, Maire M, Belongie S et al. Microsoft COCO: common objects in context[M]. ∥Fleet D, Pajdla T, Schiele B, et al. Computer Vision-ECCV 2014. Cham: Springer, 8693, 740-755(2014).

Lin T Y, Maire M, Belongie S et al. Microsoft COCO: common objects in context[M]. ∥Fleet D, Pajdla T, Schiele B, et al. Computer Vision-ECCV 2014. Cham: Springer, 8693, 740-755(2014).

[13] Xu Y Z, Yao X J, Li X et al[J]. Object detection in high resolution remote sensing images based on fully convolution networks Bulletin of Surveying and Mapping, 2018, 77-82.

Xu Y Z, Yao X J, Li X et al[J]. Object detection in high resolution remote sensing images based on fully convolution networks Bulletin of Surveying and Mapping, 2018, 77-82.

[14] Zhang Z Y. Plane detection in optical remote sensing images based on deep learning[D]. Xiamen: Xiamen University, 20-30(2016).

Zhang Z Y. Plane detection in optical remote sensing images based on deep learning[D]. Xiamen: Xiamen University, 20-30(2016).

[15] Wang J C, Tan X C, Wang Z H et al. Faster R-CNN deep learning network based object recognition of remote sensing image[J]. Journal of Geo-Information Science, 20, 1500-1508(2018).

Wang J C, Tan X C, Wang Z H et al. Faster R-CNN deep learning network based object recognition of remote sensing image[J]. Journal of Geo-Information Science, 20, 1500-1508(2018).

[16] Feng X Y, Mei W, Hu D S. Aerial target detection based on improved faster R-CNN[J]. Acta Optica Sinica, 38, 0615004(2018).

Feng X Y, Mei W, Hu D S. Aerial target detection based on improved faster R-CNN[J]. Acta Optica Sinica, 38, 0615004(2018).

[17] Lin T Y, Dollár P, Girshick R et al. Feature pyramid networks for object detection. [C]∥2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), July 21-26, 2017, Honolulu, HI, USA. New York: IEEE, 936-944(2017).

Lin T Y, Dollár P, Girshick R et al. Feature pyramid networks for object detection. [C]∥2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), July 21-26, 2017, Honolulu, HI, USA. New York: IEEE, 936-944(2017).

[18] Huang G, Liu Z. Maaten L V D, et al. Densely connected convolutional networks. [C]∥2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), July 21-26, 2017, Honolulu, HI, USA. New York: IEEE, 2261-2269(2017).

Huang G, Liu Z. Maaten L V D, et al. Densely connected convolutional networks. [C]∥2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), July 21-26, 2017, Honolulu, HI, USA. New York: IEEE, 2261-2269(2017).

[19] Simonyan K. -04-10)[2018-12-22]. https: ∥arxiv., org/abs/1409, 1556(2015).

Simonyan K. -04-10)[2018-12-22]. https: ∥arxiv., org/abs/1409, 1556(2015).

[20] Ioffe S, Szegedy C. Batch normalization: accelerating deep network training by reducing internal covariate shift. [C]∥ICML'15 Proceedings of the 32nd International Conference on International Conference on Machine Learning, July 6-11, 2015, Lille, France. Massachusetts: JMLR. org, 448-456(2015).

Ioffe S, Szegedy C. Batch normalization: accelerating deep network training by reducing internal covariate shift. [C]∥ICML'15 Proceedings of the 32nd International Conference on International Conference on Machine Learning, July 6-11, 2015, Lille, France. Massachusetts: JMLR. org, 448-456(2015).

[21] He K M, Zhang X Y, Ren S Q et al. Deep residual learning for image recognition. [C]∥2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 27-30, 2016, Las Vegas, NV, USA. New York: IEEE, 770-778(2016).

He K M, Zhang X Y, Ren S Q et al. Deep residual learning for image recognition. [C]∥2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 27-30, 2016, Las Vegas, NV, USA. New York: IEEE, 770-778(2016).

[22] Parker J A, Kenyon R V, Troxel D E. Comparison of interpolating methods for image resampling[J]. IEEE Transactions on Medical Imaging, 2, 31-39(1983). http://europepmc.org/abstract/MED/18234586

Parker J A, Kenyon R V, Troxel D E. Comparison of interpolating methods for image resampling[J]. IEEE Transactions on Medical Imaging, 2, 31-39(1983). http://europepmc.org/abstract/MED/18234586

[23] de Boer P T, Kroese D P, Mannor S et al. . A tutorial on the cross-entropy method[J]. Annals of Operations Research, 134, 19-67(2005). http://link.springer.com/article/10.1007/s10479-005-5724-z

de Boer P T, Kroese D P, Mannor S et al. . A tutorial on the cross-entropy method[J]. Annals of Operations Research, 134, 19-67(2005). http://link.springer.com/article/10.1007/s10479-005-5724-z

[24] Yosinski J, Clune J, Bengio Y et al. -11-06)[2018-12-22]. https:∥arxiv., org/abs/1411, 1792(2014).

Yosinski J, Clune J, Bengio Y et al. -11-06)[2018-12-22]. https:∥arxiv., org/abs/1411, 1792(2014).

[25] Everingham M, van Gool L, Williams C K I et al. . The pascal visual object classes (VOC) challenge[J]. International Journal of Computer Vision, 88, 303-338(2010).

Everingham M, van Gool L, Williams C K I et al. . The pascal visual object classes (VOC) challenge[J]. International Journal of Computer Vision, 88, 303-338(2010).

[26] Loshchilov I. -03-03)[2018-12-25]. https:∥arxiv., org/abs/1608, 03983(2017).

Loshchilov I. -03-03)[2018-12-25]. https:∥arxiv., org/abs/1608, 03983(2017).

[27] Szegedy C, Vanhoucke V, Ioffe S et al. Rethinking the inception architecture for computer vision. [C]∥2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 27-30, 2016, Las Vegas, NV, USA. New York: IEEE, 2818-2826(2016).

Szegedy C, Vanhoucke V, Ioffe S et al. Rethinking the inception architecture for computer vision. [C]∥2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 27-30, 2016, Las Vegas, NV, USA. New York: IEEE, 2818-2826(2016).