[1] 邵延华, 张铎, 楚红雨, 等. 基于深度学习的YOLO目标检测综述[J]. 电子与信息学报, 2022, 44(10): 3697-3708.
SHAO Y H, ZHANG D, CHU H Y, et al. A review of YOLO object detection based on deep learning[J]. Journal of Electronics and Information, 2022, 44(10): 3697-3708.
[2] LIU W, ANGUELOV D, ERHAN D, et al. SSD: single shot multibox detector[C]//Proceedings of the European Conference on Computer Vision, 2016: 21-37.
[3] TIAN Z, SHEN C, CHEN H, et al. FCOS: fully convolutional one-stage object detection[C]//Proceedings of the IEEE International Conference on Computer Vision, 2019: 9627-9636.
[4] GIRSHICK R. Fast R-CNN[C]//Proceedings of the IEEE International Conference on Computer Vision, 2015: 1440-1448.
[5] 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 & Machine Intelligence, 2017, 39(6): 1137-1149.
[6] 刘春磊, 陈天恩, 王聪, 等. 小样本目标检测研究综述[J]. 计算机科学与探索, 2023, 17(1): 53-73.
LIU C L, CHEN T E, WANG C, et al. Survey of few-shot object detection[J]. Journal of Frontiers of Computer Science & Technology, 2023, 17(1): 53-73.
[7] 王若萱, 吴建平, 徐辉. 自动驾驶汽车感知系统仿真的研究及应用综述[J]. 系统仿真学报, 2022, 34(12): 2507-2521.
WANG R X, WU J P, XU H. Overview of research and application of autonomous vehicle sensing system simulation[J]. Journal of System Simulation, 2022, 34(12): 2507-2521.
[8] WEI Z, ZHANG F, CHANG S, et al. MmWave radar and vision fusion for object detection in autonomous driving: a review[J]. arXiv:2108.03004, 2021.
[9] KUMAWAT H, MUKHOPADHYAY S. Radar guided dynamic visual attention for resource-efficient RGB object detection[C]//Proceedings of the 2022 International Joint Conference on Neural Networks, 2022: 1-8.
[10] XIAO P, GUO J S, DU J, et al. Pedestrian detection based on fusion of millimeter wave radar and vision[C]//Proceedings of the 2018 International Conference on Artificial Intelligence and Pattern Recognition, 2018: 38-42.
[11] LONG N, WANG K, CHENG R, et al. Fusion of millimeter wave radar and RGB-Depth sensors for assisted navigation of the visually impaired[C]//Proceedings of the Conference on Millimetre Wave and Terahertz Sensors and Technology, 2018.
[12] LV P, WANG B, CHENG F, et al. Multi-objective association detection of farmland obstacles based on information fusion of millimeter wave radar and camera[J]. Sensors, 2023, 23(1): 230.
[13] SONG Y, XIE Z, WANG X, et al. MS-YOLO: object detection based on YOLOv5 optimized fusion millimeter-wave radar and machine vision[J]. IEEE Sensors Journal, 2022, 22(15): 15435-15447.
[14] NOBIS F, GEISSLINGE M, WEBE M, et al. A deep learning-based radar and camera sensor fusion architecture for object detection[J]. arXiv:2005.07431, 2020.
[15] CHANG S, ZHANG Y, ZHANG F, et al. Spatial attention fusion for obstacle detection using MmWave radar and vision sensor[J]. Sensors, 2020, 20(4): 956.
[16] HE K, ZHANG X, REN S, et al. Deep residual learning for image recognition[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2016: 770-778.
[17] STACKER L, HEIDENREICH P, RAMBACH J, et al. Fusion point pruning for optimized 2D object detection with radar-camera fusion[C]//Proceedings of the 2022 IEEE/CVF Winter Conference on Applications of Computer Vision, 2022: 1275-1282.
[18] QI C, SONG C, ZHANG N, et al. Millimeter-wave radar and vision fusion target detection algorithm based on an extended network[J]. Machines, 2022, 10(8): 675.
[19] HWANG B, LEE S, HAN H. LNFCOS: efficient object detection through deep learning based on LNblock[J]. ?Electronics, 2022, 11(17): 2783.
[20] CHOLLET F. Xception: deep learning with depthwise separable convolutions[C]//Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition, 2017: 1800-1807.
[21] FEI W, JIANG M, CHEN Q, et al. Residual attention network for image classification[C]//Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition, 2017: 6450-6458.
[22] LIU Y, SHAO Z, HOFFMANN N. Global attention mechanism: retain information to enhance channel-spatial interactions[J]. arXiv:2112.05561, 2021.
[23] 王剑哲, 吴秦. 坐标注意力特征金字塔的显著性目标检测算法[J]. 计算机科学与探索, 2023, 17(1): 154-165.
WANG J Z, WU Q. Saliency target detection algorithm based on coordinate attention feature pyramid[J]. Journal of Frontiers of Computer Science and Technology, 2023, 17(1): 154-165.
[24] 陈欣, 万敏杰, 马超, 等. 采用多尺度特征融合SSD的遥感图像小目标检测[J]. 光学精密工程, 2021, 29(11): 2672-2682.
CHEN X, WAN M J, MA C, et al. Small target detection in remote sensing image using multi-scale feature fusion SSD[J]. Optics and Precision Engineering, 2021, 29(11): 2672-2682.
[25] WOO S, PARK J, LEE J Y, et al. CBAM: convolutional block attention module[C]//Proceedings of the IEEE European Conference on Computer Vision, 2018: 3-19.
[26] ZHANG Q L, YANG Y B. SA-Net: shuffle attention for deep convolutional neural networks[C]//Proceedings of the 2021 IEEE International Conference on Acoustics, Speech and Signal Processing, 2021: 2235-2239.
[27] WANG Q, WU B, ZHU P, et al. ECA-Net: efficient channel attention for deep convolutional neural networks[C]//Proceedings of the 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2020.
[28] QIN X, LI N, WENG C, et al. Simple attention module based speaker verification with iterative noisy label detection[C]//Proceedings of the 2022 IEEE International Conference on Acoustics, Speech and Signal Processing, 2022: 6722-6726.
[29] 郑美俊, 田益民, 杨帅. 基于改进FCOS网络的遥感 目标检测[J]. 航天返回与遥感, 2022, 43(5): 133-141.
ZHENG M J, TIAN Y M, YANG S. Remote sensing target detection based on improved FCOS network[J]. Space Return and Remote Sensing, 2022, 43(5): 133-141.
[30] REZATOFIGHI H, TSOI N, GWAK J Y, et al. Generalized intersection over union: a metric and a loss for bounding box regression[C]//Proceedings of the 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2019: 658-666.
[31] ZHANG Y F, REN W, ZHANG Z, et al. Focal and efficient IOU loss for accurate bounding box regression[J]. Neurocomputing, 2022, 506: 146-157.
[32] REDMON J, FARHADI A. YOLOv3: an incremental improvement[J]. arXiv:1804.02767, 2018.
[33] BOCHKOVSKIY A, WANG C Y, LIAO H Y M. YOLOv4: optimal speed and accuracy of object detection[J]. arXiv:2004.10934, 2020.
[34] WANG C Y, YEH I H, LIAO H Y M. You only learn one representation: unified network for multiple tasks[J]. arXiv:2105.04206, 2021.
[35] LAW H, DENG J. CornerNet: detecting objects as paired keypoints[J]. International Journal of Computer Vision, 2020, 128(3): 642-656. |