| 255 | 0 | 402 |
| 下载次数 | 被引频次 | 阅读次数 |
无人机(UAV)凭借三维机动性与俯视视角，已成为智能交通系统(ITS)地面感知的关键补充。然而，航拍图像存在小目标密集、尺度漂移、透视畸变及环境域偏移等问题，给高精度检测带来挑战。聚焦智能交通场景下的无人机航拍目标检测技术，系统梳理其演进脉络、剖析核心技术并总结应用实践成果：(1)从传统特征工程方法到深度学习的两阶段、单阶段及Transformer式检测框架的演进历程，系统归纳面向ITS的典型数据集，并深入解析网络结构优化、训练策略改进、轻量化部署及RGB-红外多模态融合等关键技术；(2)全面总结无人机目标检测在交通流量监测与管理、交通安全主动防控、交通违法智能识别与执法、基础设施动态巡检等核心场景的应用现状与效能提升；(3)进一步凝练当前研究的局限性，指出任务定义与评测目标偏离、数据覆盖不均衡、跨场景泛化能力不足等瓶颈问题，并对未来研究方向进行了展望与探讨。
Abstract：Having three-dimensional(3D) moving ability and an air high-angle view, Unmanned Aerial Vehicles(UAVs) have appeared as an unreplaceable complement to ground sensing technologies in the area of Intelligent Transportation Systems(ITS). Even so, the aerial image data brings very obvious difficulties to high-accuracy object detection, which include the dense arrangement of tiny targets, size change, visual angle deformation, and environment domain transformation. This review focuses on UAV-based object detection technology in intelligent transportation scenes, this paper systematically reviews its development path, analyzes key technical problems and progress, and sums up application results, as follows in detail:(1) It follows the development course of UAV object detection technologies, from traditional manual feature engineering approaches to deep learning-based frameworks—including two-stage, single-stage, and Transformer-based detection structures. Typical data collections which are custom made for ITS situations are carried out systematic classification, and core technical directions are analyzed deeply, including network frame optimization, training method promotion, light-weight arrangement technology, and RGB-infrared multi-model combination methods.(2) It makes overall induction of the present applying situation and performance promotion of UAV object detection in core intelligent transportation scenes, for example traffic flow monitoring and management, initiative traffic safety prevention and control, intelligent identification and law execution of traffic breaking rules, and dynamic checking of transportation infrastructure foundations.(3)It further makes more precise the restrictions of current studies, pointing out key difficult points such as the non-consistency between task definition and assessment targets, uneven data covering among different situations, and the absence of universal capability under complicated environment conditions.
[1]QYResearch.2025年全球无人机行业总体规模、主要企业国内外市场占有率及排名[EB/OL].(2025-03-28)[2026-02-05].https://www.qyresearch.com.cn/reports/5587433/ drone.QYRESEARCH.Drone report 2025,global revenue,key companies market share & rank.[EB/OL].(2025-03-28)[2026-02-05].https://www.qyresearch.com.cn/reports/5587433/ drone.
[2]秦勇，孟凡腾，张紫城，等.轨道交通基础设施自主无人机智能巡检体系框架及其技术发展综述[J].北京交通大学学报，2025,49(5):145-175.QIN Y,MENG F T,ZHANG Z C,et al.Autonomous UAV-based intelligent inspection framework for rail transit infrastructure:a review of technological developments[J].Journal of Beijing Jiaotong University,2025,49(5):145-175.
[3]黄祥，吴媚，王海楠，等.基于改进ALNS的无人机固定机巢电力巡检路径规划[J].交通运输工程与信息学报，2025,23(3):130-143.HUANG X,WU M,WANG H N,et al.Power inspection path planning for UAV fixed-nest drones basedon improved ALNS[J].Journal of Transportation Engineering and Information,2025,23(3):130-143.
[4]TANG G Y,NI J J,ZHAO Y H,et al.A survey of object detection for UAVs based on deep learning[J].Remote Sensing,2024,16(1):29.
[5]冷佳旭，莫梦竟成，周应华，等.无人机视角下的目标检测研究进展.中国图象图形学报，2023,28(9):2563-2586.LENG J X,MO M J C,ZHOU Y H,et al.Research progress of object detection from UAV perspective[J].Journal of Image and Graphics,2023,28(9):2563-2586.
[6]BAKER S,MATTHEWS I.Lucas-kanade 20 years on:a unifying framework[J].International Journal of Computer Vision,2004,56(3):221-255.
[7]ZOU Z X,CHEN K Y,SHI Z W,et al.Object detection in 20 years:a survey[J].Proceedings of the IEEE,2023,111(3):257-276.
[8]GIKRHICK 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.Columbus:IEEE,2014:580-587.
[9]GIRSHICK R.FAST R-CNN[C]//2015 IEEE International Conference on Computer Vision.Santiago:IEEE,2016:1440-1448.
[10]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.
[11]LIN T Y,DOLLAR P,GIRSHICK R,et al.Feature pyramid networks for object detection[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition(CVPR2017).Honolulu:IEEE,2017:936-944.
[12]HE K M,GKIOXARI G,DOLLA R,et al.Mask R-CNN[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2018,42(2):386-397.
[13]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.Salt Lake City:IEEE,2018:6154-6162.
[14]SERMANET P,EIGEN D,ZHANG X,et al.OverFeat:Integrated recognition,localization and detection using convolutional networks[EB/OL].(2013-12-21)[2026-02-05].arXiv:1312.6229,2013.
[15]LIU W,ANGUELOV D,ERHAN D,et al.SSD:single shot multiBox detector[C]//Proceedings of the European Conference on Computer Vision(ECCV 2016).Cham:Springer,2016:21-37.
[16]REDMON J,DIVVALA S,GIRSHICK R,et al.You only look once:Unified,real-time object detection[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.Piscataway:IEEE,2016:779-788.
[17]REDMON J,FARHADI A.YOLO9000:Better,faster,stronger [C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.Piscataway:IEEE,2017:7263-7271.
[18]REDMON J,FARHADI A.YOLOv3:An incremental improvement[EB/OL].(2018-04-08)[2026-02-05].arXiv:1804.02767,2018.
[19]Bochkovskiy A,Wang C Y,Liao H Y M.YOLOv4:optimal speed and accuracy of object detection[J].Computer Science,2020,24(3):1018-1103.
[20]LI C Y,LI L L,JIANG H L,et al.YOLOv6:a single-stage object detection framework for industrial applications[EB/OL].(2022-09-07)[2026-02-05].https://arxiv.org/abs/2209.02976.
[21]WANG C Y,BOCHKOVSKIY A,LIAO H Y.YOLOv7:trainable bag-of-freebies sets new state-of-the-art for real-time object detectors[C]//Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition.New York:IEEE,2023:7464-7475.
[22]WANG C Y,YEH I H,LIAO H Y.YOLOv9:learning what you want to learn using programmable gradient information[EB/OL].(2024-02-29)[2026-02-05].https://arxiv.org/abs/2402.13616.
[23]WANG A,CHEN H,LIU L H,et al.YOLOv10:real-time end-to-end object detection[EB/OL].(2024-05-23)[2026-02-05].https://arxiv.org/abs/2405.14458.
[24]SAPKOTA R,CHEPPALLY R H.YOLO26:key architectural enhancements and performance benchmarking for real-time object detection[EB/OL].(2025-09-29)[2026-02-05].https://arxiv.org/pdf/2509.25164.
[25]ULTRALYTICS.Ultralytics YOLO docs[EB/OL].(2026-01-15)[2026-02-05].https://docs.ultralytics.com/models/?,accessed.
[26]CARION N,MASSA F,SYNNAEVE G,et al.End-to-end object detection with transformers[C]//Proceedings of the European Conference on Computer Vision(ECCV 2020).Cham:Springer,2020:213-229.
[27]ZHU X Z,SU W J,LU L W,et al.Deformable DETR:deformable transformers for end-to-end object detection[EB/OL].(2020-10-08)[2026-02-05]https://arxiv.org/abs/2010.04159.
[28]ZHAO Y A,LU W Y,XU S l,et al.DETRs beat YOLOs on real-time object detection[C]//2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).Seattle,WA,USA:IEEE,2024,16965-16974.
[29]ZHU P F,WEN L Y,BIAN X,et al.Vision meets drones:a challenge[EB/OL].(2018-04-20)[2026-02-05].https://arxiv.org/abs/1804.07437.
[30]YU H Y,LI G R,ZHANG W G,et al.The unmanned aerial vehicle benchmark:object detection,tracking and baseline[J].International Journal of Computer Vision,2020,128 (5):1141-1159.
[31]LEPIN F,VELASTIN S A,et al.UTUAV:a drone dataset for urban traffic analysis[J].Drones,2026,10(1):15.
[32]FENGKAIBIT.UAV-ROD.[EB/OL].(2021-04-04)[2026-02-05].https://github.com/fengkaibit/UAV-ROD.
[33]SUN Y M,CAO B,HU Q H.Drone-based RGB-infrared cross-modality vehicle detection via uncertainty-aware learning[J].IEEE Transactions on Circuits and Systems for Video Technology,2022,32(10):6700-6713.
[34]BOZCAN I,KAYACAN E.AU-AIR:a multi-modal unmanned aerial vehicle dataset for low altitude traffic surveillance[EB/OL].(2020-01-31)[2026-02-05]https://arxiv.org/abs/2001.11737.
[35]KAICHUN R.VAID:vehicle aerial imaging from drone[EB/OL].(2020-07-22)[2026-02-05].https://github.com/KaiChun-RVL/VAID_dataset.
[36]HSIEH M R,LIN Y L,HSU W H.Drone-based object counting by spatially regularized regional proposal network[C]//Proceedings of the IEEE International Conference on Computer Vision(ICCV 2017).New York:IEEE,2017:4145-4153.
[37]时义，贾世杰.基于CB-CenterNet的自动驾驶遮挡目标检测[J].大连交通大学学报，2022,43(3):115-120.SHI Y,JIA S J.Automatic driving occlusion object detection based on CB-CenterNet[J].Journal of Dalian Jiaotong University,2022,43(3):115-120.
[38]LIU S,QI L,QIN H F,et al.Path aggregation network for instance segmentation[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition(CVPR 2018).Salt Lake City:IEEE,2018:8759-8768.
[39]TAN M X,PANG R M,LE Q.EfficientDet:scalable and efficient object detection[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition(CVPR).Seattle:IEEE,2020,10781-10790.
[40]王运明，彭国都，周奕昂，等.基于DA-SSD的有轨电车轨道小目标障碍物检测算法[J].大连交通大学学报，2023,44(2):108-114.WANG Y M,PENG G D,ZHOU Y A,et al.Detection of small target obstacles on tram tracks based on DA-SSD algorithm[J].Journal of Dalian Jiaotong University,2023,44(2):108-114.
[41]吴一全，童康.基于深度学习的无人机航拍图像小目标检测研究进展[J].航空学报，2025,46(3):174-200.WU Y Q,TONG K.Research advances on deep learni-ng-based small object detection in UAV aerial images[J].Acta Aeronautica et Astronautica Sinica,2025,46(3):174-200.
[42]王鼎山，贾世杰.基于目标感知增强的无人机航拍目标检测[J].大连交通大学学报，2023,44(2):115–120.WANG D S,JIA S J.Object-aware enhanced object detection for UAV aerial images[J].Journal of Dalian Jiaotong University,2023,44(2):115-120.
[43]陈鑫影，吕硕，胡明捷.基于改进SN-RetinaNet的车辆目标检测方法[J].大连交通大学学报，2025,46(4):147-154.CHEN X Y,LYU S,HU M J.Improved SN-RetinaNet based vehicle object detection method[J].Journal of Dalian Jiaotong University,2025,46(4):147-154.
[44]WAN Z Y,LAN Y Z,XU Z D,et al.DAU-YOLO:a lightweight and effective method for small object detection in UAV images[J].Remote Sensing,2025,17(10):1768.
[45]BODLA N,SINGH B,CHELLAPPA R,et al.Soft-NMS:Improving object detection with one line of code[C]//Proceedings of the IEEE International Conference on Computer Vision(ICCV).Venice:IEEE,2017:5562-5570.
[46]ZHANG H X,LIU K,GAN Z X,et al.UAV-DETR:efficient end-to-end object detection for unmanned aerial vehicle imagery[EB/OL].(2025-01-03)[2026-02-05].https://arxiv.org/abs/2501.01855.
[47]REZATOFIGHI H,TSOI N,GWAK J,et al.Generalized intersection over union:a metric and a loss for bounding box regression[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition(CVPR).Long Beach:IEEE,2019:658-666.
[48]ZHENG Z H,WANG P,LIU W,et al.Distance-IoU loss:faster and better learning for bounding box regression[C]//Proceedings of the AAAI Conference on Artificial Intelligence.New York:AAAI,2020:12993-13000.
[49]TONG Z J,CHEN Y H,XU Z W,et al.Wise-ioU:bounding box regression loss with dynamic focusing mechanism[EB/OL].(2023-01-24)[2026-02-05].https://arxiv.org/abs/2301.10051.
[50]GE Z,LIU S T,WANG F,et al.YOLOX:exceeding YOLO series in 2021[EB/OL].(2021-07-18)[2026-02-05].https://arxiv.org/abs/2107.08430.
[51]FENG C J,ZHONG Y J,GAO Y,et al.TOOD:task-aligned one-stage object detection[C]//Proceedings of the IEEE/CVF International Conference on Computer Vision(ICCV).Montreal:IEEE,2021:3490-3499.
[52]ZHU P F,WEN L Y,DU D W,et al.VisDrone-DET2019:the vision meets drone object detection in image challenge results[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2021,43(11):4031-4044.
[53]AKYON F C,ALTINUC S O,TEMIZEL A.Slicing aided hyper inference and fine-tuning for small object detection[EB/OL].(2022-02-14)[2026-02-05].https://arxiv.org/abs/2202.06934.
[54]SOLOVYEV R,WANG W,GABRUSEVA T.Weighted boxes fusion:ensembling boxes for object detection[EB/OL].(2019-10-29)[2026-02-05].https://arxiv.org/abs/1910.13302.
[55]XIA G S,BAI X,DING J,et al.DOTA:a large-scale dataset for object detection in aerial images[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition(CVPR).Salt Lake City:IEEE,2018:3974-3983.
[56]YANG X,LIU Q Q,YAN J C,et al.R3Det:refined single-stage detector with feature refinement for rotating object[C]//Proceedings of the AAAI Conference on Artificial Intelligence.Washington,DC:AAAI,2021:3163-3171.
[57]XIE X X,CHENG G,WENG J B,et al.Oriented R-CNN for object detection[C]//Proceedings of the IEEE/CVF International Conference on Computer Vision(ICCV).Montreal:IEEE,2021:3500-3509.
[58]HAN K,WANG Y H,TIAN Q,et al.GhostNet:more features from cheap operations[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition(CVPR).Seattle:IEEE,2020:1577-1586.
[59]ROSS D,BORGHI G,VEZZANI R.TakuNet:an energyefficient CNN for realtime inference on embedded UAV systems in emergency response scenarios[EB/OL].(2025-01-10)[2026-02-05].https://arxiv.org/abs/2501.05880.
[60]FENG H T,ZHANG L,ZHANG S Q,et al.RTDOD:a large-scale RGB-thermal domain-incremental object detection dataset for UAVs[J].Image and Vision Computing,2023,140:104856.
[61]YUAN M X,WANG Y Y,WEI X X.Translation,scale and rotation:cross-modal alignment meets RGB-infrared vehicle detection[C]//European Conference on Computer Vision(ECCV 2022).Cham:Springer,2022:509-525.
[62]OUYANG J L,WANG Q W,SHANG Y,et al.An RGB-TIR dataset from UAV platform for robust urban traffic scenes semantic segmentation[J].Scientific Data,2025,12(1):1701.
[63]BALTRUSAITIS T,AHUJA C,MORENCY L P.Multimodal machine learning:a survey and taxonomy[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2019,41(2):423-443.
[64]CHEN K Y,LIU J Q,ZHANG H.Illumination-guided RGB-T object detection with transformers[J].Knowledge-Based Systems,2023,268:110423.
[65]ZHANG X,ZHANG X H,WANG J H,et al.Target-aware fusion for RGB-T pedestrian detection[J].IEEE Transactions on Neural Networks and Learning Systems,2025,36(7):13276-13290.
[66]HUANG N C,YANG Y,ZHANG Q,et al.Lightweight cross-modal transformer for RGB-D salient object detection[J].Computer Vision and Image Understanding,2024,249:104148.
[67]WANG L,CHEN F L,YIN H M.Detecting and tracking vehicles in traffic by unmanned aerial vehicles[J].Automation in Construction,2016,72:294-308.
[68]汪嘉淇，黄洁玲，郭建钢，等.城市过江桥梁连续合流区最小间距研究[J].大连交通大学学报，2024,45(3):31-37.WANG J Q,HUANG J L,GUO J G,et al.Study on minimum space of continuous merging zone of urban river crossing bridges[J].Journal of Dalian Jiaotong University,2024,45(3):31-37.
[69]BYUN S,SHIN I K,MOON J,et al.Road traffic monitoring from UAV images using deep learning networks[J].Remote Sensing,2021,13(20):4027.
[70]BERGHAUS M,LAMBERTY S,EHLERS J,et al.Vehicle trajectory dataset from drone videos including off-ramp and congested traffic-analysis of data quality,traffic flow,and accident risk.[J].Communications in Transportation Research,2024,4:100133.
[71]TILON S M,NEX F.Vehicle tracking and speed estimation from unmanned aerial vehicles using segmentation-initialised trackers[C]//ISPRS Annals of the Photogrammetry,Remote Sensing and Spatial Information Sciences.Cairo:Copernicus,2023:431-437.
[72]KABASHKIN I,KULMURZINA A,NADIMOV B,et al.Synchronized multi-point UAV-based traffic monitoring for urban infrastructure decision support[J].Drones,2025,9(5):370.
[73]MESSENGER R,ISLAM M Z,WHITLOCK M,et al.Real-time traffic end-of-queue detection and tracking in UAV video[J].International Journal of Intelligent Transportation Systems Research,2023,21(3):493-505.
[74]LI B,KOURTELLIS A,CAO R,et al.DARTS:a drone-based AI-powered real-time traffic incident detection system[EB/OL].(2025-10-29)[2026-02-05].https://arxiv.org/abs/2510.26004.
[75]OUTAY F,MENGASH H A,ADNAN M.Applications of unmanned aerial vehicle(UAV)in road safety,traffic and highway infrastructure management:Recent advances and challenges[J].Transportation Research Part A:Policy and Practice,2020,141:116-129.
[76]KHANPOUR A,WANG T,VAHIDI S A,et al.UAV-based intelligent traffic surveillance system:real-time vehicle detection,classification,tracking,and behavioral analysis[EB/OL].(2025-09-04)[2026-02-05].https://arxiv.org/abs/2509.04624.
[77]SHUKLA P,SHUKLA S.Unmanned aerial vehicle(UAV)based disaster detection and crowd sensing using deep learning models[C]//Proceedings of the 26th International Conference on Distributed Computing and Networking(ICDCN 2025).New York:Association for Computing Machinery,2025:414-419.
[78]王晨，周威，章世祥.一种特征融合的视频事故快速检测方法[J].交通运输工程与信息学报，2022,20(1):31-38.WANG C,ZHOU W,ZHANG S X.A feature fusion deep learning framework for video-based crash detection systems[J].Journal of Transportation Engineering and Information,2022,20(1):31-38.
[79]PENG C F,HSIEH J W,LEU S W,et al.Drone-based vacant parking space detection[C]//Proceedings of the 32nd IEEE International Conference on Advanced Information Networking and Applications Workshops(WAINA 2018).Krakow:IEEE,2018:618-622.
[80]BENJDIRA B,KOUBAA A,BOULILA W,et al.Parking analytics framework using deep learning[EB/OL].(2022-03-15)[2026-02-05].https://arxiv.org/pdf/2203.07792.
[81]WEI W,CHEN H Y,GONG J Y,et al.Real-time parking space detection based on deep learning and panoramic images[J].Sensors,2025,25:6449.
[82]YILDIZLI T,BAYRAKTAR M,GULDUR E B.Automated road damage detection using UAVs and deep learning:A scalable solution for infrastructure maintenance[J].e-Journal of Nondestructive Testing,2025,30(10):5701.
[83]LI H X,CHEN R H,NYIRANDAYISABYE R,et al.An automatic detection method for road damage in UAV images based on multi-level perception and feature aggregation[J].Advanced Engineering Informatics,2026,69:103814.
[84]NVIDIA.Best practices—NVIDIA TensorRT[EB/OL].(2026-04-07)[2026-02-05].https://docs.nvidia.com/deeplearning/tensorrt/latest/performance/best-practices.html.
[85]BUI M D,LEE J,CHOI K,et al.UAV-based pipeline for road marking condition assessment and localization[EB/OL].(2025-11-28)[2026-02-05].https://www.preprints.org/manuscript/202511.2296/v1.
[86]LYU C,LIN S Q,LYNCH A,et al.UAV-based deep learning applications for automated inspection of civil infrastructure[J].Automation in Construction,2025,177:106285.
[87]PHAN T N,NGUYEN H H,HA T T H,et al.Deep learning models for UAV-assisted bridge inspection:a YOLO benchmark analysis[EB/OL].(2024-11-07)[2026-02-05].https://arxiv.org/html/2411.04475v1.
[88]LIU N Q,XU X,SU Y Y,et al.CLIP-guided source-free object detection in aerial images[EB/OL].(2024-01-10)[2026-02-05].https://arxiv.org/abs/2401.05168.
[89]HUA W,CHEN Q L.A survey of small object detection based on deep learning in aerial images[J].Artificial Intelligence Review,2025,58:162.
[90]HENDRYCKS D,DIETTERICH T.Benchmarking neural network robustness to common corruptions and perturbations[EB/OL].(2019-03-28)[2026-02-05].https://arxiv.org/abs/1903.12261.
[91]LIANG J,HE R,TAN T N.A comprehensive survey on test-time adaptation under distribution shifts[J].International Journal of Computer Vision,2025,133:31-64.
[92]RUAN X Q,TANG W.Fully test-time adaptation for object detection[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops(CVPRW).Seattle:IEEE,2024:1038-1047.
[93]REN T N,JIANG Q,LIU S,et al.Advance the edge of open-set object detection[EB/OL].(2024-05-16)[2026-02-05].https://arxiv.org/abs/2405.10300.
[94]LI L N H,ZHANG P C,ZHANG H T,et al.Grounded language-image pre-training[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition(CVPR2022).New Orleans:IEEE,2022:10955-10965.
[95]袁毅，王振华，滕志伟，等.面向高速公路流量预测的自适应图注意力网络[J].大连交通大学学报，2023,44(4):7-13.YUAN Y,WANG Z H,TENG Z W,et al.Study on safety risk classification and application of road traffic accident sections[J].Journal of Dalian Jiaotong University,2023,44(4):7-13.
[96]NIKOUEI M,BAROUTIAN B,NABAVI S,et al.Small object detection:a comprehensive survey on challenges,techniques and real-world applications[J].Intelligent Systems with Applications,2025,27:200561.
基本信息:
DOI：10.13291/j.cnki.djdxac.2026.03.001
中图分类号:U495;TP391.41
引用信息:
[1]贾世杰,张惠迪.面向智能交通的无人机航拍目标检测技术综述[J].大连交通大学学报,2026,47(03):1-17.DOI:10.13291/j.cnki.djdxac.2026.03.001.
基金信息:
辽宁省教育厅科学研究项目(LJKMZ20220826); 辽宁省交通厅科技计划项目(SZJT19)
2026-02-05
2026
2026-03-08
2026-03-12
2026
1
2026-06-09
2026-06-09
2026-06-09