[1] X Z CHEN, H M MA, J WAN et al. Multi-view 3D object detection network for autonomous driving, 6526-6534(2017).

[2] 2郭保青， 余祖俊， 张楠， 等. 铁路场景三维点云分割与分类识别算法［J］. 仪器仪表学报， 2017， 38（9）： 2103-2111. doi: 10.3969/j.issn.0254-3087.2017.09.002GUOB Q， YUZ J， ZHANGN， et al. 3D point cloud segmentation， classification and recognition algorithm of railway scene［J］. Chinese Journal of Scientific Instrument， 2017， 38（9）： 2103-2111. （in Chinese）. doi: 10.3969/j.issn.0254-3087.2017.09.002

[3] K M HE, X Y ZHANG, S Q REN et al. Spatial pyramid pooling in deep convolutional networks for visual recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence, 37, 1904-1916(2015).

[4] R GIRSHICK, J DONAHUE, T DARRELL et al. Rich feature hierarchies for accurate object detection and semantic segmentation. OH, 580-587(2014).

[5] R GIRSHICK. Fast R-CNN, 1440-1448(2015).

[6] S Q REN, K M HE, R GIRSHICK et al. Faster R-CNN： towards real-time object detection with region proposal networks. IEEE Transactions on Pattern Analysis and Machine Intelligence, 39, 1137-1149(2017).

[7] J REDMON, S DIVVALA, R GIRSHICK et al. You only look once： unified， real-time object detection, 779-788(2016).

[8] B Q GUO, G GENG, L Q ZHU et al. High-speed railway intruding object image generating with generative adversarial networks. Sensors, 19, 3075(2019).

[9] 9王建林， 付雪松， 黄展超， 等. 改进YOLOv2卷积神经网络的多类型合作目标检测［J］. 光学 精密工程， 2020， 28（1）： 251-260. doi: 10.3788/ope.20202801.0251WANGJ L， FUX S， HUANGZH CH， et al. Multi-type cooperative targets detection using improved YOLOv2 convolutional neural network［J］. Opt. Precision Eng.， 2020， 28（1）： 251-260. （in Chinese）. doi: 10.3788/ope.20202801.0251

[10] E ARNOLD, O Y AL-JARRAH, M DIANATI et al. A survey on 3D object detection methods for autonomous driving applications. IEEE Transactions on Intelligent Transportation Systems, 20, 3782-3795(2019).

[11] M SIMON, S MILZ, K AMENDE et al. Complex-YOLO： an Euler-region-proposal for real-time 3D object detection on point clouds, 197-209(2019).

[12] W ALI, S ABDELKARIM, M ZIDAN et al. YOLO3D： end-to-end real-time 3D oriented object bounding box detection from LiDAR point cloud, 716-728(2019).

[13] B YANG, W J LUO, R URTASUN. PIXOR： real-time 3D object detection from point clouds, 7652-7660(2018).

[14] F N IANDOLA, S HAN, M W MOSKEWICZ et al. SqueezeNet： AlexNet-level accuracy with, 5. https：//www.researchgate.net/publication/301878495_SqueezeNet_AlexNet-level_accuracy_with_50x_fewer_parameters_and_05MB_model_size

[15] Y ZHOU, O TUZEL. VoxelNet： end-to-end learning for point cloud based 3D object detection, 4490-4499(2018).

[16] Y YAN, Y X MAO, B LI. SECOND： sparsely embedded convolutional detection. Sensors, 18, 3337(2018).

[17] M LIANG, B YANG, Y CHEN et al. Multi-task multi-sensor fusion for 3D object detection, 7337-7345(2019).

[18] T E WU, C C TSAI, J N GUO. LiDAR/camera sensor fusion technology for pedestrian detection, 1675-1678(2017).

[19] R Q CHARLES, S HAO, K C MO et al. PointNet： deep learning on point sets for 3D classification and segmentation, 77-85(2017).

[20] M EVERINGHAM, L GOOL, C K I WILLIAMS et al. The pascal visual object classes （VOC） challenge. International Journal of Computer Vision, 88, 303-338(2010).

[21] T Y LIN, P DOLLÁR, R GIRSHICK et al. Feature pyramid networks for object detection, 936-944(2017).

[22] H REZATOFIGHI, N TSOI, J GWAK et al. Generalized intersection over union： a metric and a loss for bounding box regression, 658-666(2019).

[23] Z H ZHENG, P WANG, W LIU et al. Distance-IoU loss： faster and better learning for bounding box regression. Proceedings of the AAAI Conference on Artificial Intelligence, 34, 12993-13000(2020).

[24] Z X WANG, K JIA. Frustum ConvNet： sliding Frustums to aggregate local point-wise features for amodal 3D object detection, 1742-1749(2019).

[25] T Y LIN, P GOYAL, R GIRSHICK et al. Focal loss for dense object detection, 2999-3007(2017).

[26] J H YU, Y N JIANG, Z Y WANG et al. UnitBox： an advanced object detection network, 516-520(2016).

[27] D F ZHOU, J FANG, X B SONG et al. IoU loss for 2D/3D object detection, 85-94(2019).

[28] M LIANG, B YANG, S L WANG et al. Deep Continuous Fusion for Multi-sensor 3D Object Detection, 663-678(2018).

[29] Z T YANG, Y N SUN, S LIU et al. IPOD： intensive point-based object detector for point cloud(2018). https：//www.researchgate.net/publication/329641724_IPOD_Intensive_Point-based_Object_Detector_for_Point_Cloud

[30] A H LANG, S VORA, H CAESAR et al. PointPillars： fast encoders for object detection from point clouds, 12689-12697(2019).

[31] C R QI, W LIU, C X WU et al. Frustum PointNets for 3D object detection from RGB-D data, 918-927(2018).