Real-Time Detection Based on Improved Single Shot MultiBox Detector

Lili Chen; Zhengdao Zhang; Li Peng

doi:10.3788/LOP56.011002

[1] Girshick R, Donahue J, Darrell T et al. Rich feature hierarchies for accurate object detection and semantic segmentation. [C]∥IEEE Conference on Computer Vision and Pattern Recognition, 580-587(2014).

[2] Girshick R. Fast R-CNN. [C]∥IEEE International Conference on Computer Vision (ICCV), 1440-1448(2015).

[3] 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://doi.ieeecomputersociety.org/10.1109/TPAMI.2016.2577031

[4] DaiJ, LiY, HeK, et al. R-FCN: object detection via region-based fully convolutional networks[J]. arXiv preprint arXiv:1605.06409, 2016.

[5] Redmon J, Divvala S, Girshick R et al. You only look once: unified, real-time object detection. [C]∥ IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 779-788(2016).

[6] Redmon J, Farhadi A. YOLO9000: better, faster, stronger. [C]∥IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 6517-6525(2017).

[7] Liu W, Anguelov D, Erhan D et al. SSD:single shot multibox detector. [C]∥European Conference on Computer Vision, 21-37(2016).

[8] Dalal N, Triggs B. Histograms of oriented gradients for Human detection. [C]∥IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05), 886-893(2005).

[9] Lowe D G. Distinctive image features from scale-invariant keypoints[J]. International Journal of Computer Vision, 60, 91-110(2004). http://doi.ieeecomputersociety.org/resolve?ref_id=doi:10.1023/B:VISI.0000029664.99615.94&rfr_id=trans/tp/2008/10/ttp2008101683.htm

[10] Felzenszwalb P. McAllester D, Ramanan D. A discriminatively trained, multiscale, deformable part model. [C]∥IEEE Conference on Computer Vision and Pattern Recognition, 1-8(2008).

[11] Azizpour H, Laptev I. Object detection using strongly-supervised deformable part models. [C]∥ European Conference on Computer Vision, 836-849(2014).

[12] Dollar P, Appel R, Belongie S et al. Fast feature pyramids for object detection[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 36, 1532-1545(2014). http://www.ncbi.nlm.nih.gov/pubmed/26353336

[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] 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, 37, 1904-1916(2015). http://www.sciencedirect.com/science/article/pii/S0031320315004252

[15] Cai Z, Vasconcelos N. Cascade R-CNN: delving into high quality object detection. [C]∥Computer Vision and Pattern Recognition, 6154-6162(2018).

[16] Ye G L, Sun S Y, Gao K J et al. Nighttime pedestrian detection based on faster region convolution neural network[J]. Laser & Optoelectronics Progress, 54, 081003(2017).

[17] Huang X Y, Xu J L, Guo G et al. Real-time pedestrian reidentification based on enhanced aggregated channel features[J]. Laser & Optoelectronics Progress, 54, 091001(2017).

[18] Lu Y S, Li Y X, Liu B et al. Hyperspectral data haze monitoring based on deep residual network[J]. Acta Optica Sinica, 37, 1128001(2017).

[19] Redmon J, Farhadi A. YOLOv3: an incremental improvement. [C]∥Computer Vision and Pattern Recognition(2018).

[20] Lin T Y, Goyal P, Girshick R et al. Focal loss for dense object detection. [C]∥IEEE International Conference on Computer Vision (ICCV), 2999-3007(2017).

[21] Fu C Y, Liu W, Ranga A et al. DSSD: deconvolutional single shot detector. [C]∥Computer Vision and Pattern Recognition(2017).

[22] Cao G M, Xie X M, Yang W Z et al. Feature-fused SSD: fast detection for small objects[J]. Proceedings of SPIE, 106151E(2018). http://cn.arxiv.org/abs/1709.05054

[23] Jeong J, Park H, Kwak N. Enhancement of SSD by concatenating feature maps for object detection. [C]∥ British Machine Vision Conference(2017).

[24] Hosang J, Benenson R, Schiele B. A convnet for non-maximum suppression. [C]∥German Conference on Pattern Recognition, 192-204(2016).

[25] Shrivastava A, Gupta A, Girshick R. Training region-based object detectors with online hard example mining. [C]∥IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 761-769(2016).

[26] Jia Y Q, Shelhamer E, Donahue J et al. Caffe. [C]∥Proceedings of the ACM International Conference on Multimedia(2014).

[27] Simonyan K[J]. Zisserman A. Very Deep Convolutional Networks for Large-Scale Image Recognition. arXiv preprint arXiv, 1556, 2014(1409).

[28] He K M, Zhang X Y, Ren S Q et al. Deep residual learning for image recognition. [C]∥ IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 770-778(2016).

[29] Huang G, Liu Z. Maaten L V D, et al. Densely connected convolutional networks. [C]∥ IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2261-2269(2017).

[30] Zhang X, Zhou X, Lin M et al. ShuffleNet: an extremely efficient convolutional neural network for mobile devices. [C]∥ Computer Vision and Pattern Recognition(2018).

[31] Sandler M, Howard A, Zhu M et al. Inverted residuals and linear bottlenecks: mobile networks for classification, detection and segmentation. [C]∥Computer Vision and Pattern Recognition(2018).

[32] Ioffe S, Szegedy C. Batch normalization: accelerating deep network training by reducing internal covariate shift. [C]∥International Conference on Machine Learning, 448-456(2015).