[1] 乔维高,徐学进.无人驾驶汽车的发展现状及方向[J].上海汽车,2007(7):40-43.(QIAO W G, XU X J. The development situation and direction of the driverless vehicle[J]. Shanghai Auto, 2007(7):40-43.) [2] 李晓明,郎文辉,马忠磊,等.基于图像处理的井下机车行人检测技术[J].煤矿机械,2017,38(4):167-170.(LI X M, LANG W H, MA Z L, et al. Pedestrian detection technology for mine locomotive based on image processing[J]. Coal Mine Machinery, 2017, 38(4):167-170.) [3] LIU T, FU H Y, WEN Q, et al. Extended faster R-CNN for long distance human detection:finding pedestrians in UAV images[C]//Proceedings of the 2018 IEEE International Conference on Consumer Electronics. Piscataway, NJ:IEEE, 2018:1-2. [4] GIRSHICK R, DONAHUE J, DARRELL T, et al. Rich feature hierarchies for accurate object detection and semantic segmentation[C]//Proceedings of the 2014 IEEE Conference on Computer Vision and Pattern Recognition. Piscataway, NJ:IEEE, 2014:580-587. [5] HE K, ZHANG X, REN S, 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. [6] GIRSHICK R. Fast R-CNN[C]//Proceedings of the 2015 IEEE International Conference on Computer Vision. Piscataway, NJ:IEEE, 2015:1440-1448. [7] REDMON J, FARHADI A. YOLO9000:better, faster, stronger[C]//Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition. Piscataway, NJ:IEEE, 2017:6517-6525. [8] 郑嘉祺.基于DCNN的井下行人检测系统的研究与设计[D].西安:西安科技大学,2017:84-87.(ZHENG J Q. Research and design on pedestrian detection system under the mine based on DCNN[D]. Xi'an:Xi'an University of Science and Technology, 2017:84-87.) [9] REDMON J, DIWALA S, GIRSHICK R, et al. You only look once:unified, real-time object detection[C]//Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition. Washington, DC:IEEE Computer Society, 2016:779-788. [10] 王琳,卫晨,李伟山,等.结合金字塔池化模块的YOLOv2的井下行人检测[EB/OL].[2018-05-21]. https://www.doc88.com/p-0714870779937.html.(WANG L, WEI C, LI W S, et al. Pedestrian detection based on YOLOv2 with pyramid pooling module in underground coal mine[EB/OL].[2018-05-21]. https://www.doc88.com/p-0714870779937.html.) [11] 李伟山,卫晨,王琳.改进的Faster R-CNN煤矿井下行人检测算法[EB/OL].[2018-07-15]. http://kns.cnki.net/kcms/detail/11.2127.TP.20180522.0944.002.html.(LI W S, WEI C, WANG L. An improved faster R-CNN approach for pedestrian detection in underground coal mine[EB/OL].[2018-07-15]. http://kns.cnki.net/kcms/detail/11.2127.TP.20180522.0944.002.html.) [12] REN S, HE K, 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, 2015, 39(6):1137-1149. [13] 沈彤,刘文波,王京.基于双目立体视觉的目标测距系统[J].电子测量技术,2015,38(4):52-54.(SHEN T, LIU W B, WANG J. Distance measurement system based on binocular stereo vision[J]. Electronic Measurement Technology, 2015, 38(4):52-54.) [14] 郭磊,徐友春,李克强,等.基于单目视觉的实时测距方法研究[J]. 中国图象图形学报,2006,11(1):74-81.(GUO L, XU Y C, LI K Q, et al. Study on real-time distance detection based on monocular vision technique[J]. Journal of Image and Graphics, 2006, 11(1):74-81.) [15] BAO D, WANG P. Vehicle distance detection based on monocular vision[C]//Proceedings of the 2016 International Conference on Progress in Informatics and Computing. Piscataway, NJ:IEEE, 2016:187-191. [16] LIN T Y, GOYAL P, GIRSHICK R, et al. Focal loss for dense object detection[C]//Proceedings of the 2017 IEEE International Conference on Computer Vision. Washington, DC:IEEE Computer Society, 2017:2999-3007. |