[1] T Y LIN, P GOYAL, R GIRSHICK et al. Focal loss for dense object detection, 2980-2988(2017).
[2] Jianwei QIN, Chuanxu WANG, Xiaoshan FU. AF-Center:multi-modal 3D object detection method with adaptive voxel-painting fusion and Gaussian center sample assignment. Application Research of Computers, 40, 634-640(2023).
[3] Qinglin WANG, Hui LI, Lizhi XIE et al. Research on improving vehicle target detection algorithm based on lidar point cloud. Electronic Measurement Technology, 46, 120-126(2023).
[4] L ZHAO, H L HUJIE, A YONGPENG et al. Deep learning based on semantic segmentation for three-dimensional object detection from point clouds. Chinese Journal of Lasers, 48, 1710004(2021).
[5] Qiao LI, Yaochen LI, Yulong ZHANG et al. A Single-stage point cloud 3D object detection method using sparse 3D convolution. Journal of Xi'an Jiaotong University, 56, 112-122(2022).
[6] C R QI, H SU, K MO et al. Pointnet: deep learning on point sets for 3d classification and segmentation, 652-660(2017).
[7] C R QI, L YI, H SU et al. PointNet++: deep hierarchical feature learning on point sets in a metric space, 5100-5109(2017).
[8] S SHI, X WANG, H LI. Pointrcnn: 3d object proposal generation and detection from point cloud, 770-779(2019).
[9] Z YANG, Y SUN, S LIU et al. 3dssd: point-based 3d single stage object detector, 11040-11048(2020).
[10] Y ZHOU, O TUZEL. Voxelnet: end-to-end learning for point cloud based 3d object detection, 4490-4499(2018).
[11] Ruilong LI, Chuan WU, Ming ZHU. 3D object detection in voxelized point cloud scene. Chinese Journal of Liquid Crystals and Displays, 37, 1355-1363(2022).
[12] Y YAN, Y MAO, B LI. Second: sparsely embedded convolutional detection. Sensors, 18, 3337(2018).
[13] A H LANG, S VORA, H CAESAR et al. Pointpillars: fast encoders for object detection from point clouds, 12697-12705(2019).
[14] S ZHOU, Z TIAN, X CHU et al. FastPillars: a deployment-friendly Pillar-based 3D detector. Arxiv Preprint, 2023.
[15] Zhongquan WANG. Research on object detection and path planning based on deep learning(2021).
[16] Weiqin ZHAN, Rongrong NI, Biao YANG. An attention-based PointPillars+3D object detection. Journal of Jiangsu University, 41, 268-273(2020).
[17] M FENG, L ZHANG, X LIN et al. Point attention network for semantic segmentation of 3D point clouds. Pattern Recognition, 107, 107446(2020).
[18] C Y WANG, H Y M LIAO, Y H WU et al. CSPNet: A new backbone that can enhance learning capability of CNN, 390-391(2020).
[19] V MNIH, N HEESS, A GRAVES et al. Recurrent models of visual attention, 2204-2212(2014).
[20] J FU, J LIU, H TIAN et al. Dual attention network for scene segmentation, 3146-3154(2019).
[21] Tong QIN, Xiaoming LI. Longitudinal tear detection based on improved YOLOv4 for conveyor belt. Computer Systems&Applications, 32, 186-194(2023).
[22] A GEIGER, P LENZ, C STILLER et al. Vision meets robotics: the kitti dataset. The International Journal of Robotics Research, 32, 1231-1237(2013).
[23] Zhuang HAN, Tao WANG, Kun XU et al. Unsupervised monocular depth estimation with full-scale feature fusion. Journal of Qiqihar University, 39, 25-30, 43(2023).
[24] X CHEN, H MA, J WAN et al. Multi-view 3d object detection network for autonomous driving, 1907-1915(2017).
[25] C R QI, W LIU, C WU et al. Frustum pointnets for 3d object detection from rgb-d data, 918-927(2018).
[26] Z LIU, X ZHAO, T HUANG et al. Tanet: robust 3d object detection from point clouds with triple attention, 34, 11677-11684(2020).
[27] H YI, S SHI, M DING et al. Segvoxelnet: exploring semantic context and depth-aware features for 3d vehicle detection from point cloud, 2274-2280(2020).
[28] S SHI, Z WANG, J SHI et al. From points to parts: 3D object detection from point cloud with part-aware and part-aggregation network. IEEE Transactions on Pattern Analysis and Machine Intelligence, 43, 2647-2664(2020).
[29] Shun WANG. Research on 3D target detection algorithm based on lidar point cloud(2022).
[30] C HE, H ZENG, J HUANG et al. Structure aware single-stage 3d object detection from point cloud, 11873-11882(2020).
