Object Detection Algorithm Based on Improved Faster R-CNN

Bing Zhou; Runxin Li; Zhenhong Shang; Xiaowu Li

doi:10.3788/LOP57.101009

[1] Franke U, Gavrila D, Gorzig S et al. Autonomous driving goes downtown[J]. IEEE Intelligent Systems, 13, 40-48(1998).

[2] Girshick R, Donahue J, Darrell T et al. Rich feature hierarchies for accurate object detection and semantic segmentation. [C]∥2014 IEEE Conference on Computer Vision and Pattern Recognition, June 23-28, 2014. Columbus, OH, USA. New York: IEEE, 580-587(2014).

[3] Uijlings J R R, Gevers T et al. Selective search for object recognition[J]. International Journal of Computer Vision, 104, 154-171(2013).

[4] Krizhevsky A, Sutskever I, Hinton G E. Imagenet classification with deep convolutional neural networks[C]. Advances in Neural Information Processing Systems: Proceedings of the 25th International Conference on Neural Information Processing Systems-Volume 1, New York: Curran Associates Inc.5, 1097-1105(2012).

[5] Girshick R. Fast R-CNN. [C]∥2015 IEEE International Conference on Computer Vision, 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).

[7] Chen X L, Gupta A[2019-07-30]. An implementation of faster RCNN with study for region sampling [2019-07-30].https:∥arxiv., org/abs/1702, 02138.

[8] Neubeck A, van Gool L. Efficient non-maximum suppression. [C]∥18th International Conference on Pattern Recognition, August 20-24, 2006, Hong Kong, China. New York: IEEE, 3, 850-855(2006).

[9] Bodla N, Singh B, Chellappa R et al[2019-08-01]. Improving object detection with one line of code [2019-08-01].https:∥arxiv., org/abs/1704, 04503.

[10] Qiao T, Su H S, Liu G H et al. Object detection algorithm based on improved feature extraction network[J]. Laser & Optoelectronics Progress, 56, 231008(2019).

[11] He Z C, Zhao L Z, Chen C. Convolution neural network with multi-resolution feature fusion for facial expression recognition[J]. Laser & Optoelectronics Progress, 55, 071503(2018).

[12] Zhou S R, Tan F, Zeng D J. Human action recognition based on multi-size pooling of convolutional neural network[J]. Journal of Chinese Computer Systems, 38, 1893-1898(2017).

[13] 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).

[14] 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. Lecture notes in computer science. Cham: Springer, 8693, 740-755(2014).

[15] Simonyan K, Zisserman A[2019-08-14]. Very deep convolutional networks for large-scale image recognition[2019-08-14].https:∥arxiv., org/abs/1409, 1556.

[16] Deng J, Dong W, Socher R et al. ImageNet: a large-scale hierarchical image database. [C]∥2009 IEEE Conference on Computer Vision and Pattern Recognition, June 20-25, 2009, Miami, FL. New York: IEEE, 248-255(2009).

[17] Chen L L, Zhang Z D, Peng L. Real-time detection based on improvedsingle shot MultiBox detector[J]. Laser & Optoelectronics Progress, 56, 011002(2019).

[18] Bell S, Zitnick C L, Bala K et al. Inside-outside net: detecting objects in context with skip pooling and recurrent neural networks. [C]∥2016 IEEE Conference on Computer Vision and Pattern Recognition , June 27-30, 2016, Las Vegas, NV, USA. New York: IEEE, 3, 850-855(2016).

[19] Redmon J, Farhadi A[2019-02-10]. YOLO9000: better, faster, stronger.[2019-02-10]. https:∥ arx iv., org/abs/1612, 08242.