Improved algorithm of Faster R-CNN based on double threshold-non-maximum suppression

Hou Zhiqiang; Liu Xiaoyi; Yu Wangsheng; Ma Sugang

doi:10.12086/oee.2019.190159

[1] Borji A, Cheng M M, Jiang H Z, et al. Salient object detection: a benchmark[J]. IEEE Transactions on Image Processing, 2015, 24(12): 5706–5722.

[2] Luo H B, Xu L Y, Hui B, et al. Status and prospect of target tracking based on deep learning[J]. Infrared and Laser Engineering, 2017, 46(5): 0502002.

[3] Hou Z Q, Han C Z. A survey of visual tracking[J]. Acta Automatica Sinica, 2006, 32(4): 603–617.

[4] Xin P, Xu Y L, Tang H, et al. Fast airplane detection based on multi-layer feature fusion of fully convolutional networks[J]. Acta Optica Sinica, 2018, 38(3): 0315003.

[5] Dai W C, Jin L X, Li G N, et al. Real-time airplane detection algorithm in remote-sensing images based on improved YOLOv3[J]. Opto-Electronic Engineering, 2018, 45(12): 180350.

[6] Wang S M, Han L L. Moving object detection under complex dynamic background[J]. Opto-Electronic Engineering, 2018, 45(10): 180008.

[7] Zhou X Y, Liu J, Lu X, et al. A method for pedestrian detection by combining textual and visual information[J]. Acta Electronica Sinica, 2017, 45(1): 140–146.

[8] Cao M W, Yu Y. Moving object detection based on multi-layer background model[J]. Acta Electronica Sinica, 2016, 44(9): 2126–2133.

[9] Zhang Z S, Qiao S Y, Xie C H, et al. Single-shot object detection with enriched semantics[C]//Proceedings of IEEE/CVF Conference on Computer Vision and Pattern Recognition, Salt Lake City, 2018: 5813–5821.

[10] Redmon J, Divvala S, Girshick R, et al. You only look once: unified, real-time object detection[C]//Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, 2016: 779–788.

[11] Liu W, Anguelov D, Erhan D, et al. SSD: single shot MultiBox detector[EB/OL]. (2016-12-29) [2019-05-28]. arXiv: 1512. 02325 v1. https://arxiv.org/abs/1512.02325v1.

[12] Girshick R, Donahue J, Darrell T, et al. Rich feature hierarchies for accurate object detection and semantic segmentation[C]//Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, Columbus, USA, 2014: 580–587.

[13] He K M, Zhang X Y, Ren S Q, et al. Spatial pyramid pooling in deep convolutional networks for visual recognition[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2015, 37(9): 1904–1916.

[14] Girshick R. Fast R-CNN[C]//Proceedings of IEEE International Conference on Computer Vision, Santiago, Chile, 2015: 1440–1448.

[15] Ren S, He K M, Girshick R, et al. Faster R-CNN: towards real-time object detection with region proposal networks[EB/OL]. (2015-06-04)[2019-05-28]. arXiv: 1506.01497. https://arxiv.org/ abs/1506.01497?source=post_page.

[16] Bodla N, Singh B, Chellappa R, et al. Soft-NMS – improving object detection with one line of code[C]//Proceedings of IEEE International Conference on Computer Vision, Venice, Italy, 2017: 5562–5570.

[17] He K M, Gkioxari G, Dollár P, et al. Mask R-CNN[C]// Proceedings of IEEE International Conference on Computer Vision, Venice, Italy, 2017: 2980–2988.

[18] Wang X L, Shrivastava A, Gupta A. A-Fast-RCNN: hard positive generation via adversary for object detection[C]//Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, USA, 2017: 3039–3048.

[19] Kong T, Sun F C, Yao A B, et al. RON: reverse connection with objectness prior networks for object detection[C]//Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, USA, 2017: 5244–5252.

[20] Redmon J, Farhadi A. YOLO9000: better, faster, stronger[C]//Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, USA, 2017: 6517–6525.

[21] Lin T Y, Goyal P, Girshick R, et al. Focal loss for dense object detection[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2018, doi: 10.1109/TPAMI.2018.2858826.

[22] Redmon J, Farhadi A. YOLOv3: an incremental improvement[EB/OL]. (2018-04-08)[2019-05-28]. arXiv: 1804.02767. https://arxiv.org/abs/1804.02767.

[23] He K M, Zhang X Y, Ren S Q, et al. Deep residual learning for image recognition[C]//Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, USA, 2016: 770–778.

[24] Huang G, Liu Z, van der Maaten L, et al. Densely connected convolutional networks[C]//Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, USA, 2017: 2261–2269.

CLP Journals