[1] GirshickR, DonahueJ, DarrellT, et al. Rich feature hierarchies for accurate object detection and semantic segmentation[C]∥IEEE Conference on Computer Vision and Pattern Recognition, 2014: 580- 587.

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

[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, 2017, 39(6): 1137-1149.

[4] DaiJ, LiY, HeK, et al. R-FCN: object detection via region-based fully convolutional networks[J]. arXiv preprint arXiv: 1605. 06409, 2016.

[5] RedmonJ, DivvalaS, GirshickR, et al. You only look once: unified, real-time object detection[C]∥ IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016: 779- 788.

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

[7] LiuW, AnguelovD, ErhanD, et al. SSD:single shot multibox detector[C]∥European Conference on Computer Vision, 2016: 21- 37.

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

[9] Lowe D G. Distinctive image features from scale-invariant keypoints[J]. International Journal of Computer Vision, 2004, 60(2): 91-110.

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

[11] AzizpourH, LaptevI. Object detection using strongly-supervised deformable part models[C]∥ European Conference on Computer Vision, 2014: 836- 849.

[12] Dollar P, Appel R, Belongie S, et al. Fast feature pyramids for object detection[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2014, 36(8): 1532-1545.

[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, 2010, 88(2): 303-338.

[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, 2015, 37(9): 1904-1916.

[15] CaiZ, VasconcelosN. Cascade R-CNN: delving into high quality object detection[C]∥Computer Vision and Pattern Recognition, 2018: 6154- 6162.

[16] 叶国林, 孙韶媛, 高凯珺, 等. 基于加速区域卷积神经网络的夜间行人检测研究[J]. 激光与光电子学进展, 2017, 54(8): 081003.

    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, 2017, 54(8): 081003.

[17] 黄新宇, 许娇龙, 郭纲, 等. 基于增强聚合通道特征的实时行人重识别[J]. 激光与光电子学进展, 2017, 54(9): 091001.

    Huang X Y, Xu J L, Guo G, et al. Real-time pedestrian reidentification based on enhanced aggregated channel features[J]. Laser & Optoelectronics Progress, 2017, 54(9): 091001.

[18] 陆永帅, 李元祥, 刘波, 等. 基于深度残差网络的高光谱遥感数据霾监测[J]. 光学学报, 2017, 37(11): 1128001.

    Lu Y S, Li Y X, Liu B, et al. Hyperspectral data haze monitoring based on deep residual network[J]. Acta Optica Sinica, 2017, 37(11): 1128001.

[19] RedmonJ, FarhadiA. YOLOv3: an incremental improvement[C]∥Computer Vision and Pattern Recognition, 2018.

[20] Lin TY, GoyalP, GirshickR, et al. Focal loss for dense object detection[C]∥IEEE International Conference on Computer Vision (ICCV), 2017: 2999- 3007.

[21] Fu CY, LiuW, RangaA, 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, 2018, 106151E.

[23] JeongJ, ParkH, KwakN. Enhancement of SSD by concatenating feature maps for object detection[C]∥ British Machine Vision Conference, 2017.

[24] HosangJ, BenensonR, SchieleB. A convnet for non-maximum suppression[C]∥German Conference on Pattern Recognition, 2016: 192- 204.

[25] ShrivastavaA, GuptaA, GirshickR. Training region-based object detectors with online hard example mining[C]∥IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016: 761- 769.

[26] Jia YQ, ShelhamerE, DonahueJ, et al. Caffe[C]∥Proceedings of the ACM International Conference on Multimedia, 2014.

[27] SimonyanK, Zisserman A. Very Deep Convolutional Networks for Large-Scale Image Recognition[J]. arXiv preprint arXiv: 1409. 1556, 2014.

[28] He KM, Zhang XY, Ren SQ, et al. Deep residual learning for image recognition[C]∥ IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016: 770- 778.

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

[30] ZhangX, ZhouX, LinM, et al. ShuffleNet: an extremely efficient convolutional neural network for mobile devices[C]∥ Computer Vision and Pattern Recognition, 2018.

[31] SandlerM, HowardA, ZhuM, et al. Inverted residuals and linear bottlenecks: mobile networks for classification, detection and segmentation[C]∥Computer Vision and Pattern Recognition, 2018.

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