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Publishing Language: Chinese

Research progress on deep learning methods for object detection and semantic segmentation in UAV aerial images

Xudong LUO, Yiquan WU( ), Jinlin CHEN
College of Electronic and Information Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China
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Abstract

Unmanned Aerial Vehicles (UAV) have been widely used due to their advantages of small size, light weight, and simple operation. The combination of the deep learning method and UAV system can help to quickly and accurately detect the desired target on UAV aerial images with high definition and a wide field of view. Related topics have become one of the current research hotspots. This paper reviews the research progress of deep learning methods for object detection and semantic segmentation in UAV aerial images in the past ten years. First, the characteristics and wide range of application scenarios of UAVs and their aerial images are summarized, and the development process of target detection and semantic segmentation methods for UAV aerial images is briefly described. Then, the object detection and semantic segmentation methods of UAV aerial images based on deep learning are classified according to different network models, and their improvement strategies, application scenarios, contributions, and limitations are summarized. Subsequently, the data sets of aerial images taken by UAVs in recent years are collected and sorted out, and the evaluation indicators of commonly used convolutional neural network models are summarized. Finally, the existing problems in this field are pointed out, and prospects for future research trends are presented.

Keywords

Unmanned Aerial Vehicle (UAV)deep learningobject detectionsemantic segmentationdata setperformance evaluation indicator
CLC number: V279; TP391 Document code: A

References

1
JIANG B, QU R K, LI Y D, et al. Object detection in UAV imagery based on deep learning: Review [J]. Acta Aeronautica et Astronautica Sinica, 2021, 42 (4): 524519 (in Chinese).
Google Scholar
2
LIU C Y, WU Y Q. Research progress of vision detection methods based on deep learning for transmission lines [J]. Proceedings of the CSEE, 2023, 43 (19): 7423-7446 (in Chinese).
Google Scholar
3
OSCO L P, DOS SANTOS DE ARRUDA M, GONÇALVES D N, et al. A CNN approach to simultaneously count plants and detect plantation-rows from UAV imagery [J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2021, 174: 1-17.
Crossref Google Scholar
4
VEERANAMPALAYAM SIVAKUMAR A N, LI J T, SCOTT S, et al. Comparison of object detection and patch-based classification deep learning models on mid- to late-season weed detection in UAV imagery [J]. Remote Sensing, 2020, 12 (13): 2136.
Crossref Google Scholar
5
WU J T, YANG G J, YANG H, et al. Extracting apple tree crown information from remote imagery using deep learning [J]. Computers and Electronics in Agriculture, 2020, 174: 105504.
Crossref Google Scholar
6
ZHU Z J, LIU Q, CHEN H F, et al. Infrared small vehicle detection based on parallel fusion network [J]. Acta Photonica Sinica, 2022, 51 (2): 190-202 (in Chinese).
Google Scholar
7
LIU Y C, SHI G, LI Y X, et al. M-YOLO based detection and recognition of highway surface oil filling with unmanned aerial vehicle [C] //2022 7th International Conference on Intelligent Computing and Signal Processing (ICSP). Piscataway: IEEE Press, 2022: 1884-1887.
Crossref
8
DING W, ZHANG L. Building detection in remote sensing image based on improved YOLOv5 [C] //2021 17th International Conference on Computational Intelligence and Security (CIS). Piscataway: IEEE Press, 2021: 133-136.
Crossref
9
ZHANG R, WEN C B. SOD-YOLO: A small target defect detection algorithm for wind turbine blades based on improved YOLOv5 [J]. Advanced Theory and Simulations, 2022, 5 (7): 2100631
Crossref Google Scholar
10
GUO J, XIE J H, YUAN J Z, et al. Fault identification of transmission line shockproof hammer based on improved YOLOV4 [C] //2021 International Conference on Intelligent Computing, Automation and Applications (ICAA). Piscataway: IEEE Press, 2021: 826-833.
Crossref
11
LIU C Y, WU Y Q, LIU J J, et al. MTI-YOLO: A light-weight and real-time deep neural network for insulator detection in complex aerial images [J]. Energies, 2021, 14 (5): 1426.
Crossref Google Scholar
12
SAMBOLEK S, IVASIC-KOS M. Automatic person detection in search and rescue operations using deep CNN detectors [J]. IEEE Access, 2021, 9: 37905-37922.
Crossref Google Scholar
13
BOŽIĆ-ŠTULIĆ D, MARUŠIĆ Ž, GOTOVAC S. Deep learning approach in aerial imagery for supporting land search and rescue missions [J]. International Journal of Computer Vision, 2019, 127 (9): 1256-1278.
Crossref Google Scholar
14
SUO W K, HU W G, WU X S, et al. Research on autonomous landing of UAV based on optical vision [J]. Laser Journal, 2019, 40 (4): 9-13 (in Chinese).
Google Scholar
15
ZHANG Z Y, CAO Y F, FAN Y M. Research progress of vision based aerospace conflict sensing technologies for small unmanned aerial vehicle in low altitude [J]. Acta Aeronautica et Astronautica Sinica, 2022, 43 (8): 025645 (in Chinese).
Google Scholar
16
LOWE D G. Distinctive image features from scale-invariant keypoints [J]. International Journal of Computer Vision, 2004, 60 (2): 91-110.
Crossref Google Scholar
17
DALAL N, TRIGGS B. Histograms of oriented gradients for human detection [C] //2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05). Piscataway: IEEE Press, 2005: 886-893.
Crossref
18
FELZENSZWALB P F, GIRSHICK R B, MCALLES-TER D, et al. Object detection with discriminatively trained part-based models [J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2010, 32 (9): 1627-1645.
Crossref Google Scholar
19
VIOLA P, JONES M J. Robust real-time face detection [J]. International Journal of Computer Vision, 2004, 57 (2): 137-154.
Crossref Google Scholar
20
PAPAGEORGIOU C, POGGIO T. A trainable system for object detection [J]. International Journal of Computer Vision, 2000, 38 (1): 15-33.
Crossref Google Scholar
21
MARDIA K V, HAINSWORTH T J. A spatial thresholding method for image segmentation [J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1988, 10 (6): 919-927.
Crossref Google Scholar
22
ADAMS R, BISCHOF L. Seeded region growing [J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1994, 16 (6): 641-647.
Crossref Google Scholar
23
GIANNAKEAS N, KARVELIS P S, EXARCHOS T P, et al. Segmentation of microarray images using pixel classification—comparison with clustering-based methods [J]. Computers in Biology and Medicine, 2013, 43 (6): 705-716.
Crossref Google Scholar
24
GIRSHICK R, DONAHUE J, DARRELL T, et al. Rich feature hierarchies for accurate object detection and semantic segmentation [C] //2014 IEEE Conference on Computer Vision and Pattern Recognition. Piscataway: IEEE Press, 2014: 580-587.
Crossref
25
LONG J, SHELHAMER E, DARRELL T. Fully convolutional networks for semantic segmentation [C] //Proceedings of the IEEE conference on computer vision and pattern recognition. Piscataway: IEEE Press, 2015: 3431-3440.
Crossref
26
LUO X D, WU Y Q, ZHAO L Y. YOLOD: A target detection method for UAV aerial imagery [J]. Remote Sensing, 2022, 14 (14): 3240.
Crossref Google Scholar
27
LUO X D, WU Y Q, WANG F Y. Target detection method of UAV aerial imagery based on improved YOLOv5 [J]. Remote Sensing, 2022, 14 (19): 5063.
Crossref Google Scholar
28
MITTAL P, SINGH R, SHARMA A. Deep learning-based object detection in low-altitude UAV datasets: A survey [J]. Image and Vision Computing, 2020, 104: 104046.
Crossref Google Scholar
29
DIEZ Y, KENTSCH S, FUKUDA M, et al. Deep learning in forestry using UAV-acquired RGB data: A practical review [J]. Remote Sensing, 2021, 13 (14): 2837.
Crossref Google Scholar
30
ZHU P F, WEN L Y, DU D W, et al. Detection and tracking meet drones challenge [J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2022, 44 (11): 7380-7399.
Crossref Google Scholar
31
CHENG Q, FAN M, LI Y D, et al. Review on semantic segmentation of UAV aerial images [J]. Computer Engineering and Applications, 2021, 57 (19): 57-69 (in Chinese).
Google Scholar
32
LI Z H, WANG Z P, HE Y T. Aerial-photography dense small target detection algorithm based on adaptive cooperative attention mechanism [J]. Acta Aeronautica et Astronautica Sinica, 2023, 44 (13): 244-254 (in Chinese).
Google Scholar
33
WANG D W, HU L C, FANG J, et al. Small object detection algorithm based on improved Double-Head RCNN for UAV aerial images [J/OL]. Journal of Beijing University of Aeronautics and Astronautics. (2023-01-10) [2023-03-13]. https://doi.org/10.13700/j.bh.1001-5965.2022.0591 (in Chinese).
Google Scholar
34
XIE X L, LI C X, YANG X G, et al. Dynamic receptive field-based object detection in aerial imaging [J]. Acta Optica Sinica, 2020, 40 (4): 107-119 (in Chinese).
Crossref Google Scholar
35
ZHANG Z, YI H H, ZHENG J. Focusing on small objects detector in aerial images [J]. Acta Electronica Sinica, 2023, 51 (4): 944-955 (in Chinese).
Google Scholar
36
OJALA T, PIETIKÄINEN M, HARWOOD D. A comparative study of texture measures with classification based on featured distributions [J]. Pattern Recognition, 1996, 29 (1): 51-59.
Crossref Google Scholar
37
GIRSHICK R. Fast R-CNN [C] //2015 IEEE International Conference on Computer Vision (ICCV). Piscataway: IEEE Press, 2016: 1440-1448.
Crossref
38
REN S Q, HE K M, 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, 2017, 39 (6): 1137-1149.
Crossref Google Scholar
39
CAI Z W, VASCONCELOS N. Cascade R-CNN: Delving into high quality object detection [C] //2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Piscataway: IEEE Press, 2018: 6154-6162.
Crossref
40
REDMON J, DIVVALA S, GIRSHICK R, et al. You only look once: Unified, real-time object detection [C] //2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Piscataway: IEEE Press, 2016: 779-788.
Crossref
41
LIU W, ANGUELOV D, ERHAN D, et al. SSD: Single shot MultiBox detector [C] //European Conference on Computer Vision. Cham: Springer, 2016: 21-37.
Crossref
42
LIN T Y, GOYAL P, GIRSHICK R, et al. Focal loss for dense object detection [J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2020, 42 (2): 318-327.
Crossref Google Scholar
43
TAN M X, PANG R M, LE Q V. EfficientDet: Scalable and efficient object detection [C] //2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). Piscataway: IEEE Press, 2020: 10778-10787.
Crossref
44
ZHOU X Y, KOLTUN V, KRÄHENBÜHL P. Tracking objects as points [C] //VEDALDI A, BISCHOF H, BROX T, et al. European Conference on Computer Vision. Cham: Springer, 2020: 474-490.
Crossref
45
TIAN Z, SHEN C H, CHEN H, et al. FCOS: Fully convolutional one-stage object detection [C] //2019 IEEE/CVF International Conference on Computer Vision (ICCV). Piscataway: IEEE Press, 2020: 9626-9635.
Crossref
46
RONNEBERGER O, FISCHER P, BROX T. U-net: Convolutional networks for biomedical image segmentation [C] //International Conference on Medical Image Computing and Computer-Assisted Intervention. Cham: Springer, 2015: 234-241.
Crossref
47
BADRINARAYANAN V, KENDALL A, CIPOLLA R. SegNet: A deep convolutional encoder-decoder architecture for image segmentation [J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2017, 39 (12): 2481-2495.
Crossref Google Scholar
48
HE K M, GKIOXARI G, DOLLÁR P, et al. Mask R-CNN [C] //16th IEEE International Conference on Computer Vision (ICCV). Piscataway: IEEE Press, 2017: 2961-2969.
Crossref
49
HE K M, ZHANG X Y, REN S Q, 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.
Crossref Google Scholar
50
XI X S, XIA K, YANG Y H, et al. Urban individual tree crown detection research using multispectral image dimensionality reduction with deep learning [J]. National Remote Sensing Bulletin, 2022, 26 (4): 711-721 (in Chinese).
Google Scholar
51
DU L, SUN Y Q, CHEN S, et al. A novel object detection model based on Faster R-CNN for spodoptera frugiperda according to feeding trace of corn leaves [J]. Agriculture, 2022, 12 (2): 248.
Crossref Google Scholar
52
TANG J P, WANG J, WANG H L, et al. Insulator defect detection based on improved Faster R-CNN [C] // 2022 4th Asia Energy and Electrical Engineering Symposium (AEEES). Piscataway: IEEE Press, 2022: 541-546.
Crossref
53
WANG M, LUO X, WANG X, et al. Research on vehicle detection based on Faster R-CNN for UAV images [C] //IGARSS 2020-2020 IEEE International Geoscience and Remote Sensing Symposium. Piscataway: IEEE Press, 2021: 1177-1180.
Crossref
54
AVOLA D, CINQUE L, DIKO A, et al. MS-Faster R-CNN: Multi-stream backbone for improved Faster R-CNN object detection and aerial tracking from UAV images [J]. Remote Sensing, 2021, 13 (9): 1670.
Crossref Google Scholar
55
DING J J, ZHANG J H, ZHAN Z Q, et al. A precision efficient method for collapsed building detection in post-earthquake UAV images based on the improved NMS algorithm and Faster R-CNN [J]. Remote Sensing, 2022, 14 (3): 663.
Crossref Google Scholar
56
ZHOU Q F, DING S Q, QING G W, et al. UAV vision detection method for crane surface cracks based on Faster R-CNN and image segmentation [J]. Journal of Civil Structural Health Monitoring, 2022, 12 (4): 845-855.
Crossref Google Scholar
57
HUANG H, LI L L, MA H B. An improved Cascade R-CNN-based target detection algorithm for UAV aerial images [C] //2022 7th International Conference on Image, Vision and Computing (ICIVC). Piscataway: IEEE Press, 2022: 232-237.
Crossref
58
BAO W X, REN Y X, LIANG D, et al. Defect detection algorithm of anti-vibration hammer based on improved Cascade R-CNN [C] //2020 International Conference on Intelligent Computing and Human-Computer Interaction (ICHCI). Piscataway: IEEE Press, 2021: 294-297.
Crossref
59
LIN Q Z, DING Y, XU H, et al. ECascade-RCNN: Enhanced Cascade RCNN for multi-scale object detection in UAV images [C] //2021 7th International Conference on Automation, Robotics and Applications (ICARA). Piscataway: IEEE Press, 2021: 268-272.
Crossref
60
LI X, LIU S N, YANG Z, et al. Multi-target detection of transmission lines based on improved Cascade R-CNN [J]. Journal of Electronic Measurement and Instrumentation, 2021, 35 (10): 24-32 (in Chinese).
Google Scholar
61
ZHANG R Q, SHAO Z F, PORTNOV A, et al. Multi-scale dilated convolutional neural network for object detection in UAV images [J]. Geomatics and Information Science of Wuhan University, 2020, 45 (6): 895-903 (in Chinese).
Google Scholar
62
REDMON J, FARHADI A. YOLO9000: Better, faster, stronger [C] //2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Piscataway: IEEE Press, 2017: 6517-6525.
Crossref
63
LIN M, CHEN Q, YAN S. Network in network [DB/OL]. arXiv preprint: 1312.4400, 2013.
Google Scholar
64
REDMON J, FARHADI A. YOLOv3: An incremental improvement [DB/OL]. arXiv preprint: 1804.02767, 2018.
Google Scholar
65
BOCHKOVSKIY A, WANG C Y, LIAO H Y M. YOLOv4: Optimal speed and accuracy of object detection [DB/OL]. arXiv preprint: 2004.10934, 2020.
Google Scholar
66
GE Z, LIU S, WANG F, et al. YOLOx: Exceeding YOLO series in 2021 [DB/OL]. arXiv preprint: 2107.08430, 2021.
Google Scholar
67
RAMPRIYA R S, SUGANYA R, NATHAN S, et al. A comparative assessment of deep neural network models for detecting obstacles in the real time aerial railway track images [J]. Applied Artificial Intelligence, 2022, 36 (1): 2018184..
Crossref Google Scholar
68
LIU W J, QIANG J, LI X X, et al. UAV image small object detection based on composite backbone network [J]. Mobile Information Systems, 2022, 2022: 7319529.
Crossref Google Scholar
69
PEI W, XU Y M, ZHU Y Y, et al. The target detection method of aerial photography images with improved SSD [J]. Journal of Software, 2019, 30 (3): 738-758 (in Chinese).
Google Scholar
70
LI X, SONG S Q, YIN X Q. Real-time vehicle detection technology for UAV imagery based on target spatial distribution features [J]. China Journal of Highway and Transport, 2022, 35 (12): 193-204 (in Chinese).
Google Scholar
71
WANG X Q. Vehicle image detection method using deep learning in UAV video [J]. Computational Intelligence and Neuroscience, 2022, 2022: 8202535.
Crossref Google Scholar
72
JAWAHARLALNEHRU A, SAMBANDHAM T, SEKAR V, et al. Target object detection from unmanned aerial vehicle (UAV) images based on improved YOLO algorithm [J]. Electronics, 2022, 11 (15): 2343.
Crossref Google Scholar
73
JAVED M G, RAZA M, GHAFFAR M M, et al. QuantYOLO: A high-throughput and power-efficient object detection network for resource and power constrained UAVs [C] //2021 Digital Image Computing: Techniques and Applications (DICTA). Piscataway: IEEE Press, 2021: 1-8.
Crossref
74
LIU J, JIA R, LI W, et al. High precision detection algorithm based on improved RetinaNet for defect recognition of transmission lines [J]. Energy Reports, 2020, 6: 2430-2440.
Crossref Google Scholar
75
PURCELL C R, WALSH A J, COLEFAX A P, et al. Assessing the ability of deep learning techniques to perform real-time identification of shark species in live streaming video from drones [J]. Frontiers in Marine Science, 2022, 9: 981897.
Crossref Google Scholar
76
TAKAYA K, SHIBATA A, MIZUNO Y, et al. Unmanned aerial vehicles and deep learning for assessment of anthropogenic marine debris on beaches on an island in a semi-enclosed sea in Japan [J]. Environmental Research Communications, 2022, 4 (1): 015003.
Crossref Google Scholar
77
LI H Y, LI X Q, HAN X Z, et al. Cooperative object detection and recognition algorithm for multiple UAVs based on decision fusion [J]. Systems Engineering and Electronics, 2022, 44 (3): 746-754 (in Chinese).
Google Scholar
78
CHENG Z, CHEN J Y, ZHANG X, et al. Comparative study of two target detection algorithms in UAV aerial photography detection [C] //Proc SPIE 12057, Twelfth International Conference on Information Optics and Photonics, 2021, 12057: 889-894.
Crossref
79
JUNOS M H, KHAIRUDDIN A S M, THANNIRMA-LAI S, et al. Automatic detection of oil palm fruits from UAV images using an improved YOLO model [J]. The Visual Computer, 2022, 38 (7): 2341-2355.
Crossref Google Scholar
80
YANG Z S, XU Z, WANG Y H. Bidirection-fusion-YOLOv3: An improved method for insulator defect detection using UAV image [J]. IEEE Transactions on Instrumentation and Measurement, 2022, 71: 3521408.
Crossref Google Scholar
81
ZHU J Q, ZHONG J T, MA T, et al. Pavement distress detection using convolutional neural networks with images captured via UAV [J]. Automation in Construction, 2022, 133: 103991.
Crossref Google Scholar
82
SAHIN O, OZER S. YOLODrone: Improved YOLO architecture for object detection in drone images [C] //2021 44th International Conference on Telecommunications and Signal Processing (TSP). Piscataway: IEEE Press, 2021: 361-365.
Crossref
83
LIU F, HAN X. Adaptive aerial object detection based on multi-scale deep learning [J]. Acta Aeronautica et Astronautica Sinica, 2022, 43 (5): 325270 (in Chinese).
Google Scholar
84
PU L, ZHANG X J. Deep learning based UAV vision object detection and tracking [J]. Journal of Beijing University of Aeronautics and Astronautics, 2022, 48 (5): 872-880 (in Chinese).
Google Scholar
85
TSENG H H, YANG M D, SAMINATHAN R, et al. Rice seedling detection in UAV images using transfer learning and machine learning [J]. Remote Sensing, 2022, 14 (12): 2837.
Crossref Google Scholar
86
ALDAHOUL N, KARIM H A, SABRI A Q M, et al. A comparison between various human detectors and CNN-based feature extractors for human activity recognition via aerial captured video sequences [J]. IEEE Access, 2022, 10: 63532-63553.
Crossref Google Scholar
87
DOUSAI N M K, LONČARIĆ S. Detecting humans in search and rescue operations based on ensemble learning [J]. IEEE Access, 2022, 10: 26481-26492.
Crossref Google Scholar
88
XIA H Y, YANG B H, LI Y L, et al. An improved CenterNet model for insulator defect detection using aerial imagery [J]. Sensors, 2022, 22 (8): 2850.
Crossref Google Scholar
89
ALBABA B M, OZER S. SyNet: An ensemble network for object detection in UAV images [C] //2020 25th International Conference on Pattern Recognition (ICPR). Piscataway: IEEE Press, 2021: 10227-10234.
Crossref
90
LIU X, HUANG J, YANG T, et al. Improved small object detection for UAV acquisition based on CenterNet [J]. Computer Engineering and Applications, 2022, 58 (14): 96-104 (in Chinese).
Google Scholar
91
WANG S K, REN P F, LÜ X, et al. Small target detection algorithm of UAV high resolution image based on center point and dual attention mechanism [J]. Journal of Applied Sciences, 2021, 39 (4): 650-659 (in Chinese).
Google Scholar
92
AKSHATHA K R, BISWAS S, KARUNAKAR A K, et al. Anchored versus anchorless detector for car detection in aerial imagery [C] //2021 2nd Global Conference for Advancement in Technology (GCAT). Piscataway: IEEE Press, 2021: 1-6.
Crossref
93
WANG H X, HUANG Z, CHEN Y Q, et al. Defect detection from power line images using advanced deep detectors [C] //2021 13th International Conference on Wireless Communications and Signal Processing (WCSP). Piscataway: IEEE Press, 2021: 1-5.
Crossref
94
GAO Y X, HOU R M, GAO Q A, et al. A fast and accurate few-shot detector for objects with fewer pixels in drone image [J]. Electronics, 2021, 10 (7): 783.
Crossref Google Scholar
95
ZHAO L J, LIU C A, ZHANG Z, et al. Transmission line object detection method based on label adaptive allocation [J]. Mathematics, 2022, 10 (12): 2150.
Crossref Google Scholar
96
ZHANG Z, ZHENG J. Interframe target regression network for vehicle detection in UAV video [J]. Journal of Xidian University, 2021, 48 (4): 151-158 (in Chinese).
Google Scholar
97
TAN L, LV X Y, LIAN X F, et al. YOLOv4_Drone: UAV image target detection based on an improved YOLOv4 algorithm [J]. Computers & Electrical Engineering, 2021, 93: 107261.
Crossref Google Scholar
98
CHENG Y B. Detection of power line insulator based on enhanced YOLO model [C] //2022 IEEE Asia-Pacific Conference on Image Processing, Electronics and Computers (IPEC). Piscataway: IEEE Press, 2022: 626-632.
Crossref
99
BAO W X, REN Y X, WANG N A, et al. Detection of abnormal vibration dampers on transmission lines in UAV remote sensing images with PMA-YOLO [J]. Remote Sensing, 2021, 13 (20): 4134.
Crossref Google Scholar
100
ZHU Y C, ZHOU J, YANG Y H, et al. Rapid target detection of fruit trees using UAV imaging and improved light YOLOv4 algorithm [J]. Remote Sensing, 2022, 14 (17): 4324.
Crossref Google Scholar
101
HUANG L M, WANG Y X, XU Q, et al. Recognition of abnormally discolored trees caused by pine wilt disease using YOLO algorithm and UAV images [J]. Transactions of the Chinese Society of Agricultural Engineering, 2021, 37 (14): 197-203 (in Chinese).
Google Scholar
102
YU Z W, SHEN Y G, SHEN C K. A real-time detection approach for bridge cracks based on YOLOv4-FPM [J]. Automation in Construction, 2021, 122: 103514.
Crossref Google Scholar
103
WU J, SUN Y X, WANG X L. Corrosion detection method of transmission line components in mining area based on multiscale enhanced fusion [J]. Mobile Information Systems, 2022, 2022: 7408265.
Crossref Google Scholar
104
ZHU J R, WANG X D, LIU Y, et al. UavTinyDet: Tiny object detection in UAV scenes [C] //2022 7th International Conference on Image, Vision and Computing (ICIVC). Piscataway: IEEE Press, 2022: 195-200.
Crossref
105
LIU S D, LIU Y H, SUN Y M, et al. Small object detection in UAV aerial images based on inverted residual attention [J]. Journal of Beijing University of Aeronautics and Astronautics, 2023, 49 (3): 514-524 (in Chinese).
Google Scholar
106
MAO G T, DENG T M, YU N J. Object detection in UAV images based on multi-scale split attention [J]. Acta Aeronautica et Astronautica Sinica, 2023, 44 (5): 268-278 (in Chinese).
Google Scholar
107
LI Z R, NAMIKI A, SUZUKI S, et al. Application of low-altitude UAV remote sensing image object detection based on improved YOLOv5 [J]. Applied Sciences, 2022, 12 (16): 8314.
Crossref Google Scholar
108
ZHANG R, WEN C B. SOD-YOLO: A small target defect detection algorithm for wind turbine blades based on improved YOLOv5 [J]. Advanced Theory and Simulations, 2022, 5 (7): 2100631.
Crossref Google Scholar
109
BAI J P, WANG W, CHEN Y X, et al. Detection and spatial location of wind turbine blades based on lightweight YOLOv5 [J]. CAAI Transactions on Intelligent Systems, 2022, 17 (6): 1173-1181 (in Chinese).
Google Scholar
110
WANG X W, ZHAO Q Z, JIANG P, et al. LDS-YOLO: A lightweight small object detection method for dead trees from shelter forest [J]. Computers and Electronics in Agriculture, 2022, 198: 107035.
Crossref Google Scholar
111
LIU W, QUIJANO K, CRAWFORD M M. YOLOv5-tassel: Detecting tassels in RGB UAV imagery with improved YOLOv5 based on transfer learning [J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2022, 15: 8085-8094.
Crossref Google Scholar
112
BAO W X, XIE W J, HU G S, et al. Wheat ear counting method in UAV images based on TPH-YOLO [J]. Transactions of the Chinese Society of Agricultural Engineering, 2023, 39 (1): 155-161 (in Chinese).
Google Scholar
113
RU C Y, ZHANG S H, QU C N, et al. The high-precision detection method for insulators’ self-explosion defect based on the unmanned aerial vehicle with improved lightweight ECA-YOLOX-tiny model [J]. Applied Sciences, 2022, 12 (18): 9314.
Crossref Google Scholar
114
YAO Z S, LIU T, YANG T L, et al. Rapid detection of wheat ears in orthophotos from unmanned aerial vehicles in fields based on YOLOX [J]. Frontiers in Plant Science, 2022, 13: 851245.
Crossref Google Scholar
115
HOU H Y, CHEN M X, TIE Y B, et al. A universal landslide detection method in optical remote sensing images based on improved YOLOX [J]. Remote Sensing, 2022, 14 (19): 4939.
Crossref Google Scholar
116
ZHAN J L, HU Y W, CAI W W, et al. PDAM-STPNNet: A small target detection approach for wildland fire smoke through remote sensing images [J]. Symmetry, 2021, 13 (12): 2260.
Crossref Google Scholar
117
ZHONG J T, ZHU J Q, JU H Y, et al. Multi-scale feature fusion network for pixel-level pavement distress detection [J]. Automation in Construction, 2022, 141: 104436.
Crossref Google Scholar
118
QI L J, ZHOU J R, WAN J J, et al. Canopy recognition of cherry fruit tree based on SegNet network model [C] //Proc SPIE 11915, International Conference on Optics and Image Processing (ICOIP 2021), 2021, 11915: 92-104.
Crossref
119
LI G A, HAN W T, HUANG S J, et al. Extraction of sunflower lodging information based on UAV multi-spectral remote sensing and deep learning [J]. Remote Sensing, 2021, 13 (14): 2721.
Crossref Google Scholar
120
LI F, BAI J Y, ZHANG M Y, et al. Yield estimation of high-density cotton fields using low-altitude UAV imaging and deep learning [J]. Plant Methods, 2022, 18 (1): 1-11.
Crossref Google Scholar
121
JEON E I, KIM S, PARK S, et al. Semantic segmentation of seagrass habitat from drone imagery based on deep learning: A comparative study [J]. Ecological Informatics, 2021, 66: 101430.
Crossref Google Scholar
122
HUANG L, WU X Q, PENG Q Z, et al. Depth semantic segmentation of tobacco planting areas from unmanned aerial vehicle remote sensing images in plateau mountains [J]. Journal of Spectroscopy, 2021, 2021: 6687799.
Crossref Google Scholar
123
NARVARIA A, KUMAR U, JHANWWEE K S, et al. Classification and identification of crops using deep learning with UAV data [C] //2021 IEEE International India Geoscience and Remote Sensing Symposium (InGARSS). Piscataway: IEEE Press, 2022: 153-156.
Crossref
124
ZHAI Z Q, CHEN X G, ZHANG R Y, et al. Evaluation of residual plastic film pollution in pre-sowing cotton field using UAV imaging and semantic segmentation [J]. Frontiers in Plant Science, 2022, 13: 991191.
Crossref Google Scholar
125
YU J, CHENG T, CAI N, et al. Wheat lodging extraction using Improved_Unet network [J]. Frontiers in Plant Science, 2022, 13: 1009835.
Crossref Google Scholar
126
PI Y L, NATH N D, BEHZADAN A. Detection and semantic segmentation of disaster damage in UAV foot-age [J]. Journal of Computing in Civil Engineering, 2021, 35 (2): 04020063.
Crossref Google Scholar
127
ZHENG C W, ABD-ELRAHMAN A, WHITAKER V M, et al. Deep learning for strawberry canopy delineation and biomass prediction from high-resolution images [J]. Plant Phenomics, 2022, 2022: 9850486.
Crossref Google Scholar
128
LI Y B, CHAI G Q, WANG Y T, et al. ACE R-CNN: An attention complementary and edge detection-based instance segmentation algorithm for individual tree species identification using UAV RGB images and LiDAR data [J]. Remote Sensing, 2022, 14 (13): 3035.
Crossref Google Scholar
129
SINGH A K, DWIVEDI A K, SUMANTH M, et al. An efficient approach for instance segmentation of railway track sleepers in low altitude UAV images using Mask R-CNN [C] //IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium. Piscataway: IEEE Press, 2022: 4895-4898.
Crossref
130
CHEN W, CHEN C Y, LIU M, et al. Wall cracks detection in aerial images using improved Mask R-CNN [J]. Computers, Materials & Continua, 2022, 73 (1): 767-782.
Crossref Google Scholar
131
YU J Y, LI F, XUE X K, et al. Intelligent identification of bridge structural cracks based on unmanned aerial vehicle and Mask R-CNN [J]. China Journal of Highway and Transport, 2021, 34 (12): 80-90 (in Chinese).
Google Scholar
132
PUERTAS E, DE-LAS-HERAS G, FERNÁNDEZ-ANDRÉS J, et al. Dataset: Round about aerial images for vehicle detection [J]. Data, 2022, 7 (4): 47.
Crossref Google Scholar
133
GA̧SIENICA-JÓZKOWY J, KNAPIK M, CYGANEK B. An ensemble deep learning method with optimized weights for drone-based water rescue and surveillance [J]. Integrated Computer-Aided Engineering, 2021, 28 (3): 221-235.
Crossref Google Scholar
134
BEHERA T K, BAKSHI S, SA P K, et al. The NITRDrone dataset to address the challenges for road extraction from aerial images [J]. Journal of Signal Processing Systems, 2023, 95 (2): 197-209.
Crossref Google Scholar
135
DU D W, QI Y K, YU H Y, et al. The unmanned aerial vehicle benchmark: Object detection and tracking [C] //European Conference on Computer Vision. Cham: Springer, 2018: 375-391.
Crossref
136
NIGAM I, HUANG C, RAMANAN D. Ensemble knowledge transfer for semantic segmentation [C] //2018 IEEE Winter Conference on Applications of Computer Vision (WACV). Piscataway: IEEE Press, 2018: 1499-1508.
Crossref
137
ROBICQUET A, SADEGHIAN A, ALAHI A, et al. Learning social etiquette: Human trajectory understanding in crowded scenes [C] //European Conference on Computer Vision. Cham: Springer, 2016: 549-565.
Crossref
138
ROBICQUET A, ALAHI A, SADEGHIAN A, et al. Forecasting social navigation in crowded complex scenes [DB/OL]. arXiv preprint: 1601.00998, 2016.
Google Scholar
139
HSIEH M R, LIN Y L, HSU W H. Drone-based object counting by spatially regularized regional proposal network [C] //2017 IEEE International Conference on Computer Vision (ICCV). Piscataway: IEEE Press, 2017: 4165-4173.
Crossref
140
BAREKATAIN M, MARTÍ M, SHIH H F, et al. Okutama-action: An aerial view video dataset for concurrent human action detection [C] //2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW). Piscataway: IEEE Press, 2017: 2153-2160.
Crossref
141
MUELLER M, SMITH N, GHANEM B. A benchmark and simulator for UAV tracking [C] //14th European Conference on Computer Vision (ECCV). Cham: Springer, 2016: 445-461.
Crossref
142
DU D W, ZHU P F, WEN L Y, et al. VisDrone-DET2019: The vision meets drone object detection in image challenge results [C] //2019 IEEE/CVF International Conference on Computer Vision Workshop (ICCVW). Piscataway: IEEE Press, 2020: 213-226.
143
ZHU P F, DU D W, WEN L Y, et al. VisDrone-VID2019: The vision meets drone object detection in video challenge results [C] //2019 IEEE/CVF International Conference on Computer Vision Workshop (ICCVW). Piscataway: IEEE Press, 2020: 227-235.
144
GHIASI G, LIN T Y, LE Q V. NAS-FPN: Learning scalable feature pyramid architecture for object detection [C] //2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). Piscataway: IEEE Press, 2020: 7029-7038.
Crossref
Acta Aeronautica et Astronautica Sinica
Volume 45 Issue 6,
March 2024
Article number: 028822
DOI: 10.7527/S1000-6893.2023.28822
Cite this article:
LUO X, WU Y, CHEN J. Research progress on deep learning methods for object detection and semantic segmentation in UAV aerial images. Acta Aeronautica et Astronautica Sinica, 2024, 45(6): 028822. https://doi.org/10.7527/S1000-6893.2023.28822

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Received: 06 April 2023
Revised: 04 May 2023
Accepted: 01 June 2023
Published: 12 June 2023
© 2024 The Journal of Acta Aeronautica et Astronautica Sinica
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