基于深度学习的无人机目标检测研究综述
Survey of UAV Target Detection Based on Deep Learning
DOI: 10.12677/CSA.2023.135107, PDF,  被引量   
作者: 刘 宸, 李士心*, 孟范润, 陈范凯：天津职业技术师范大学电子工程学院，天津
关键词: 无人机；深度学习；目标检测；卷积神经网络；UAV； Deep Learning； Object Detection； Convolution Neural Network
摘要: 无人机具有体积小、灵活性强、航拍视野广等特点，广泛应用于警用巡查、城市交通监管、天气监测、电力巡检、应急救援救灾等行业。近年来，随着计算机视觉领域的蓬勃发展，基于深度学习的目标检测技术逐渐应用于无人机领域，并不断得到改进和加强。本文系统性地阐述了基于深度学习的目标检测技术发展历程和研究现状。针对现阶段无人机航拍影像小目标多、背景复杂、目标尺度变化大的特性，归纳和分析了近期对无人机目标检测的相关研究。最后，展望了基于深度学习的无人机目标检测技术的未来发展趋势。
Abstract: The UAV has the characteristics of small size, strong flexibility, wide range of aerial photography, and is widely used in police patrol, urban traffic supervision, weather monitoring, electric power patrol, emergency rescue and disaster relief and other industries. In recent years, with the vigorous development of the field of computer vision, the target detection technology based on deep learning has been gradually applied to the field of unmanned aerial vehicles, and has been continuously improved and strengthened. This paper first systematically expounds the development history and research status of object detection technology based on deep learning. Aiming at the characteristics of many small targets, complex background and large change of target scale in UAV aerial photography at this stage, the optimization methods of UAV target detection in recent times are summarized and analyzed. Finally, the future development trend of UAV target detection technology based on deep learning is prospected.
文章引用：刘宸, 李士心, 孟范润, 陈范凯. 基于深度学习的无人机目标检测研究综述[J]. 计算机科学与应用, 2023, 13(5): 1092-1099. https://doi.org/10.12677/CSA.2023.135107

参考文献

[1] 江波, 屈若锟, 李彦冬, 等. 基于深度学习的无人机航拍目标检测研究综述[J]. 航空学报, 2021, 42(4): 131-145.
[2] 包晓敏, 王思琪. 基于深度学习的目标检测算法综述[J]. 传感器与微系统, 2022, 41(4): 5-9. [Google Scholar] [CrossRef] 
[3] 贾俊立. 基于深度学习的无人机影像目标检测算法研究[D]: [硕士学位论文]. 大连: 大连理工大学, 2021.[CrossRef] 
[4] 杨建秀. 无人机视角下车辆目标检测的算法研究[D]: [博士学位论文]. 西安: 西安电子科技大学, 2021.[CrossRef] 
[5] Girshick, R., Donahue, J., Darrell, T., et al. (2014) Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation. 2014 IEEE Conference on Computer Vi-sion and Pattern Recognition, Columbus, 23-28 June 2014, 580-587. [Google Scholar] [CrossRef] 
[6] He, K., Zhang, X., Ren, S., et al. (2014) Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence, 37, 1904-1916. [Google Scholar] [CrossRef] 
[7] Girshick, R. (2015) Fast R-CNN. 2015 IEEE International Conference on Computer Vision (ICCV), Santiago, 7-13 December 2015, 1440-1448. [Google Scholar] [CrossRef] 
[8] Ren, S., He, K., Girshick, R., et al. (2017) Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks. IEEE Transactions on Pattern Analysis & Machine Intelli-gence, 39, 1137-1149. [Google Scholar] [CrossRef] 
[9] He, K., Gkioxari, G., Dollar, P., et al. (2017) Mask R-CNN. International Conference on Computer Vision. 2017 IEEE International Conference on Computer Vision (ICCV), Venice, 22-29 October 2017. [Google Scholar] [CrossRef] 
[10] Berg, A.C., Fu, C.Y., Szegedy, C., et al. (2015) SSD: Single Shot MultiBox Detector. 14th European Conference, Computer Vision—ECCV 2016, Amsterdam, 11-14 October 2016, 21-37.
[11] Redmon, J., Divvala, S., Girshick, R., et al. (2016) You Only Look Once: Unified, Real-Time Object Detec-tion. 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, 27-30 June 2016, 779-788. [Google Scholar] [CrossRef] 
[12] Redmon, J. and Farhadi, A. (2017) YOLO9000: Better, Faster, Stronger. 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, 21-26 July 2017, 6517-6525. [Google Scholar] [CrossRef] 
[13] Redmon, J. and Farhadi, A. (2018) YOLOv3: An In-cremental Improvement.
[14] Bochkovskiy, A., Wang, C.Y. and Liao, H. (2020) YOLOv4: Optimal Speed and Accu-racy of Object Detection.
[15] Wang, C.Y., Bochkovskiy, A. and Liao, H. (2022) YOLOv7: Trainable Bag-of-Freebies Sets New State-of-the-Art for Real-Time Object Detectors.
[16] Sommer, L.W., Schuchert, T. and Beyerer, J. (2017) Fast Deep Vehicle Detection in Aerial Images. 2017 IEEE Winter Conference on Applications of Computer Vision (WACV), Santa Rosa, 24-31 March 2017, 311-319. [Google Scholar] [CrossRef] 
[17] 李利霞, 王鑫, 王军, 张又元. 基于特征融合与注意力机制的无人机图像小目标检测算法[J/OL]. 图学学报: 1-10. http://kns.cnki.net/kcms/detail/10.1034.T.20230223.1435.002.html, 2023-03-07.
[18] Liu, Y., Yang, F. and Hu, P. (2020) Small-Object Detection in UAV-Captured Images via Multi-Branch Parallel Feature Pyramid Networks. IEEE Access, 8, 145740-145750. [Google Scholar] [CrossRef] 
[19] 张上, 张岳, 王恒涛, 等. 轻量化无人机遥感图像小目标检测算法[J/OL]. 无线电工程, 1-9. http://kns.cnki.net/kcms/detail/13.1097.tn.20230414.1639.013.html, 2023-04-19.
[20] Tan, L., Lv, X.Y., Lian, X.F. and Wang, G. (2021) YOLOv4_Drone: UAV Image Target Detection Based on an Improved YOLOv4 Algorithm. Comput-ers & Electrical Engineering, 93, Article ID: 107261.
[21] 江德港, 江智, 黄子杰, 郭彩玲, 李柏林. 基于Efficientnet的无人机车辆目标检测算法[J/OL]. 计算机工程与应用, 1-11. http://kns.cnki.net/kcms/detail/11.2127.TP.20221027.0859.002.html, 2023-03-07.
[22] 张瑞倩, 邵振峰, Aleksei Portnov, 汪家明. 多尺度空洞卷积的无人机影像目标检测方法[J]. 武汉大学学报(信息科学版), 2020, 45(6): 895-903. [Google Scholar] [CrossRef] 
[23] 罗旭鸿, 刘永春, 楚国铭, 等. 基于改进Yolov5无人机图像目标检测算法[J/OL]. 无线电工程, 1-10. http://kns.cnki.net/kcms/detail/13.1097.TN.20230411.1311.006.html, 2023-04-19.
[24] Wang, J., Ding, J., Guo, H., et al. (2019) Mask OBB: A Semantic Attention-Based Mask Oriented Bounding Box Representation for Multi-Category Object Detection in Aerial Images. Remote Sensing, 11, Article No. 2930. [Google Scholar] [CrossRef] 
[25] Yang, X., Sun, H., Fu, K., et al. (2018) Automatic Ship Detection of Re-mote Sensing Images from Google Earth in Complex Scenes Based on Multi-Scale Rotation Dense Feature Pyramid Networks. Remote Sensing, 10, Article No. 132. [Google Scholar] [CrossRef] 
[26] Singh, B. and Davis, L.S. (2018) An Analysis of Scale Invariance in Object Detection—SNIP. 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Salt Lake City, 18-23 June 2018, 3578-3587. [Google Scholar] [CrossRef] 
[27] 刘展威, 陈慈发, 董方敏. 基于YOLOv5s的航拍小目标检测改进算法研究[J/OL]. 无线电工程, 1-10. http://kns.cnki.net/kcms/detail/13.1097.TN.20230411.1645.026.html, 2023-04-19.