A Large Contextual Dataset for Classification, Detection and Counting of Cars with Deep Learning

Deep Residual Learning for Image Recognition

He, K., Zhang, X., Ren, S., & Sun, J. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.90 · Cited by 235,474 · see citing works

You Only Look Once: Unified, Real-Time Object Detection

Redmon, J., Divvala, S., Girshick, R., & Farhadi, A. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.91 · Cited by 53,803 · see citing works

Going deeper with convolutions

Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., & Rabinovich, A. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2015.7298594 · Cited by 48,562 · see citing works

Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate ShiftPreprint

Ioffe, S. (2024) · arXiv (Cornell University) · doi:10.57702/o9raffed · Cited by 15,273 · see citing works

Caffe: Convolutional Architecture for Fast Feature Embedding

Jia, Y., Shelhamer, E., Donahue, J., Karayev, S., Long, J., Girshick, R., Guadarrama, S., & Darrell, T. (2014) · Proceedings of the 22nd ACM international conference on Multimedia · doi:10.1145/2647868.2654889 · Cited by 11,077 · see citing works

Caffe: Convolutional Architecture for Fast Feature EmbeddingPreprint

Jia, Y., Shelhamer, E., Donahue, J., Karayev, S., Long, J., Girshick, R., Guadarrama, S., & Darrell, T. (2014) · arXiv (Cornell University) · doi:10.48550/arxiv.1408.5093 · Cited by 4,749 · see citing works

Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning

Szegedy, C., Ioffe, S., Vanhoucke, V., & Alemi, A. (2017) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v31i1.11231 · Cited by 4,636 · see citing works

Deep Residual Learning for Image Recognition

He, K., Zhang, X., Ren, S., & Sun, J. (2015) · arXiv · doi:10.48550/arxiv.1512.03385 · Cited by 4,196 · see citing works

Online Object Tracking: A Benchmark

Wu, Y., Lim, J., & Yang, M.-H. (2013) · 2013 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2013.312 · Cited by 4,138 · see citing works

Single-Image Crowd Counting via Multi-Column Convolutional Neural Network

Zhang, Y., Zhou, D., Chen, S., Gao, S., & Ma, Y. (2016) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2016.70 · Cited by 2,356 · see citing works

Cross-scene crowd counting via deep convolutional neural networks

Zhang, C., Li, H., Wang, X., & Yang, X. (2015) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2015.7298684 · Cited by 1,155 · see citing works

Multi-source Multi-scale Counting in Extremely Dense Crowd Images

Idrees, H., Saleemi, I., Seibert, C., & Shah, M. A. (2013) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2013.329 · Cited by 1,065 · see citing works

Learning To Count Objects in Images

Lempitsky, V., & Zisserman, A. (2010) · Cited by 1,063 · see citing works

Vehicle detection in aerial imagery : A small target detection benchmark

Razakarivony, S., & Jurie, F. (2016) · Journal of Visual Communication and Image Representation · doi:10.1016/j.jvcir.2015.11.002 · Cited by 916 · see citing works

Vehicle Detection in Satellite Images by Hybrid Deep Convolutional Neural Networks

Chen, X., Xiang, S., Liu, C., & Pan, C. (2014) · IEEE Geoscience and Remote Sensing Letters · doi:10.1109/lgrs.2014.2309695 · Cited by 619 · see citing works

Deep People Counting in Extremely Dense Crowds

Wang, C., Zhang, H., Yang, L., Liu, S., & Cao, X. (2015) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/2733373.2806337 · Cited by 395 · see citing works

Automatic Car Counting Method for Unmanned Aerial Vehicle Images

Moranduzzo, T., & Melgani, F. (2013) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2013.2253108 · Cited by 209 · see citing works

Interactive Object Counting

Arteta, C., Lempitsky, V., Noble, J. A., & Zisserman, A. (2014) · Lecture notes in computer science · doi:10.1007/978-3-319-10578-9_33 · Cited by 170 · see citing works

Visual-inertial simultaneous localization, mapping and sensor-to-sensor self-calibration

Kelly, J. W., & Sukhatme, G. S. (2009) · IEEE International Symposium on Computational Intelligence in Robotics and Automation (CIRA) · doi:10.1109/cira.2009.5423178 · Cited by 85 · see citing works

Convolutional Neural Networks for Counting Fish in Fisheries Surveillance Video

French, G., Fisher, M. H., Mackiewicz, M., & Needle, C. L. (2015) · doi:10.5244/c.29.mvab.7 · Cited by 84 · see citing works

Vehicle Detection in Satellite Images by Parallel Deep Convolutional Neural Networks

Chen, X., Xiang, S., Liu, C., & Pan, C. (2013) · Asian Conference on Pattern Recognition · doi:10.1109/acpr.2013.33 · Cited by 79 · see citing works

Learning to count with deep object features

Seguí, S., Pujol, O., & Vitria, J. M. (2015) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw.2015.7301276 · Cited by 76 · see citing works

Overhead imagery research data set — an annotated data library & tools to aid in the development of computer vision algorithms

Tanner, F. R., Colder, B. W., Pullen, C., Heagy, D., Eppolito, M., Carlan, V., Oertel, C., & Sallee, P. A. (2009) · IEEE Applied Imagery Pattern Recognition Workshop · doi:10.1109/aipr.2009.5466304 · Cited by 71 · see citing works

Object-based Detection and Classification of Vehicles from High-resolution Aerial Photography

Holt, A. C., Seto, E., Rivard, T., & Gong, P. L. (2009) · Photogrammetric Engineering & Remote Sensing · doi:10.14358/pers.75.7.871 · Cited by 49 · see citing works

Learning to count with deep object featuresPreprint

Seguí, S., Pujol, O., & Vitria, J. M. (2015) · arXiv (Cornell University) · doi:10.48550/arxiv.1505.08082 · Cited by 22 · see citing works

Aerial Car Detection and Urban Understanding

Kamenetsky, D., & Sherrah, J. (2015) · International Conference on Digital Image Computing: Techniques and Applications (DICTA) · doi:10.1109/dicta.2015.7371225 · Cited by 12 · see citing works

Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning

Szegedy, C., Ioffe, S., Vanhoucke, V., et al. (2016) · arXiv · doi:10.48550/arxiv.1602.07261

You Only Look Once: Unified, Real-Time Object Detection

Redmon, J., Divvala, S., Girshick, R., et al. (2015) · arXiv · doi:10.48550/arxiv.1506.02640

DOTA: A Large-Scale Dataset for Object Detection in Aerial Images

Xia, G.-S., Bai, X., Ding, J., Zhu, Z., Belongie, S., Luo, J., Datcu, M., Pelillo, M., & Zhang, L. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2018.00418 · Cited by 3,414 · see citing works

Object detection in optical remote sensing images: A survey and a new benchmark

Li, K., Wan, G., Cheng, G., Meng, L., & Han, J. (2020) · ISPRS Journal of Photogrammetry and Remote Sensing · doi:10.1016/j.isprsjprs.2019.11.023 · Cited by 2,317 · see citing works

Detection and tracking meet drones challenge

Zhu, P., Wen, L., Du, D., Bian, X., Fan, H., Hu, Q., & Ling, H. (2022) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2021.3119563 · Cited by 1,336 · see citing works

Drone-Based RGB-Infrared Cross-Modality Vehicle Detection Via Uncertainty-Aware Learning

Sun, Y., Cao, B., Zhu, P., & Hu, Q. (2022) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2022.3168279 · Cited by 732 · see citing works

Object Detection in Aerial Images: A Large-Scale Benchmark and Challenges

Ding, J., Xue, N., Xia, G.-S., Bai, X., Yang, W., Yang, M. Y., Belongie, S., Luo, J., Datcu, M., Pelillo, M., et al. (2022) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2021.3117983 · Cited by 678 · see citing works

Bayesian Loss for Crowd Count Estimation With Point Supervision

Ma, Z., Wei, X., Hong, X., & Gong, Y. (2019) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00624 · Cited by 668 · see citing works

A survey of recent advances in CNN-based single image crowd counting and density estimation

Sindagi, V. A., & Patel, V. M. (2017) · Pattern Recognition Letters · doi:10.1016/j.patrec.2017.07.007 · Cited by 634 · see citing works

Artificial Intelligence for Remote Sensing Data Analysis: A review of challenges and opportunities

Zhang, L., & Zhang, L. (2022) · IEEE Geoscience and Remote Sensing Magazine · doi:10.1109/mgrs.2022.3145854 · Cited by 613 · see citing works

FAIR1M: A benchmark dataset for fine-grained object recognition in high-resolution remote sensing imagery

Sun, X., Wang, P., Yan, Z., Xu, F., Wang, R., Diao, W., Chen, J., Li, J., Feng, Y., Xu, T., et al. (2022) · ISPRS Journal of Photogrammetry and Remote Sensing · doi:10.1016/j.isprsjprs.2021.12.004 · Cited by 555 · see citing works

Drone-Based Object Counting by Spatially Regularized Regional Proposal Network

Hsieh, M.-R., Lin, Y.-L., & Hsu, W. H. (2017) · 2017 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.446 · Cited by 547 · see citing works

Small-Object Detection in Remote Sensing Images with End-to-End Edge-Enhanced GAN and Object Detector Network

Rabbi, J., Ray, N., Schubert, M., Chowdhury, S., & Chao, D. K. (2020) · Remote Sensing · doi:10.3390/rs12091432 · Cited by 368 · see citing works

SpaceNet: A Remote Sensing Dataset and Challenge SeriesPreprint

Etten, A. V., Lindenbaum, D., & Bacastow, T. M. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1807.01232 · Cited by 342 · see citing works

Tackling climate change with machine learning

Rolnick, D., Donti, P. L., Kaack, L. H., Kochanski, K., Lacoste, A., Sankaran, K., Ross, A. S., Milojevic-Dupont, N., Jaques, N., Waldman-Brown, A., et al. (2023) · ACM Computing Surveys · doi:10.1145/3485128 · Cited by 336 · see citing works

Representation Learning by Learning to Count

Noroozi, M., Pirsiavash, H., & Favaro, P. (2017) · IEEE International Conference on Computer Vision · doi:10.1109/iccv.2017.628 · Cited by 379 · see citing works

The Effects of Super-Resolution on Object Detection Performance in Satellite Imagery

Shermeyer, J., & Etten, A. V. (2019) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw.2019.00184 · Cited by 298 · see citing works

Geography-Aware Self-Supervised Learning

Ayush, K., Uzkent, B., Meng, C., Tanmay, K., Burke, M., Lobell, D., & Ermon, S. (2021) · 2021 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.01002 · Cited by 288 · see citing works

FMSSD: Feature-Merged Single-Shot Detection for Multiscale Objects in Large-Scale Remote Sensing Imagery

Wang, P., Sun, X., Diao, W., & Fu, K. (2019) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2019.2954328 · Cited by 285 · see citing works

iSAID: A Large-scale Dataset for Instance Segmentation in Aerial ImagesPreprint

Zamir, S. W., Arora, A., Gupta, A., Khan, S. H., Sun, G., Khan, F. S., Zhu, F., Shao, L., Xia, G.-S., & Bai, X. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1905.12886 · Cited by 279 · see citing works

xView: Objects in Context in Overhead ImageryPreprint

Lam, D., Kuzma, R. S., McGee, K., Dooley, S. W., Laielli, M. J., Klaric, M. N., Bulatov, Y., & McCord, B. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1802.07856 · Cited by 261 · see citing works

SeaDronesSee: A Maritime Benchmark for Detecting Humans in Open Water

Varga, L. A., Kiefer, B., Messmer, M., & Zell, A. (2022) · 2022 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV) · doi:10.1109/wacv51458.2022.00374 · Cited by 253 · see citing works

On Creating Benchmark Dataset for Aerial Image Interpretation: Reviews, Guidances, and Million-AID

Long, Y., Xia, G.-S., Li, S., Yang, W., Yang, M. Y., Zhu, X. X., Zhang, L., & Li, D. (2021) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2021.3070368 · Cited by 250 · see citing works

Precise Detection in Densely Packed Scenes

Goldman, E., Herzig, R., Eisenschtat, A., Goldberger, J., & Hassner, T. (2019) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2019.00537 · Cited by 247 · see citing works

Spatiotemporal Modeling for Crowd Counting in Videos

Xiong, F., Shi, X., & Yeung, D. (2017) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.551 · Cited by 206 · see citing works

Learning To Count Everything

Ranjan, V., Sharma, U., Nguyen, T., & Hoai, M. (2021) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr46437.2021.00340 · Cited by 194 · see citing works

PBNet: Part-based convolutional neural network for complex composite object detection in remote sensing imagery

Sun, X., Wang, P., Wang, C., Liu, Y., & Fu, K. (2021) · ISPRS Journal of Photogrammetry and Remote Sensing · doi:10.1016/j.isprsjprs.2020.12.015 · Cited by 191 · see citing works

Vehicle Detection From UAV Imagery With Deep Learning: A Review

Bouguettaya, A., Zarzour, H., Kechida, A., & Taberkit, A. M. (2021) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2021.3080276 · Cited by 187 · see citing works

Deep Learning and Earth Observation to Support the Sustainable Development Goals: Current approaches, open challenges, and future opportunities

Persello, C., Wegner, J. D., Hänsch, R., Tuia, D., Ghamisi, P., Koeva, M., & Camps‐Valls, G. (2022) · IEEE Geoscience and Remote Sensing Magazine · doi:10.1109/mgrs.2021.3136100 · Cited by 197 · see citing works

Remote Sensing Object Detection Meets Deep Learning: A metareview of challenges and advances

Zhang, X., Zhang, T., Wang, G., Zhu, P., Tang, X., Jia, X., & Jiao, L. (2023) · IEEE Geoscience and Remote Sensing Magazine · doi:10.1109/mgrs.2023.3312347 · Cited by 191 · see citing works

Class-Agnostic Counting

Lu, E., Xie, W., & Zisserman, A. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-20893-6_42 · Cited by 163 · see citing works

Detecting and Tracking Small and Dense Moving Objects in Satellite Videos: A Benchmark

Yin, Q., Hu, Q., Liu, H., Zhang, F., Wang, Y., Lin, Z., An, W., & Guo, Y. (2021) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2021.3130436 · Cited by 155 · see citing works

Cattle detection and counting in UAV images based on convolutional neural networks

Shao, W. Q., Kawakami, R., Yoshihashi, R., You, S., Hidemichi, K., & Naemura, T. (2019) · International Journal of Remote Sensing · doi:10.1080/01431161.2019.1624858 · Cited by 149 · see citing works

A Survey of Computer Vision Methods for 2D Object Detection from Unmanned Aerial Vehicles

Cazzato, D., Cimarelli, C., Sánchez-López, J. L., Voos, H., & Leo, M. (2020) · Journal of Imaging · doi:10.3390/jimaging6080078 · Cited by 143 · see citing works

Kernel-Based Density Map Generation for Dense Object Counting

Wan, J., Wang, Q., & Chan, A. B. (2020) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2020.3022878 · Cited by 145 · see citing works

Vision Meets Drones: Past, Present and FuturePreprint

Zhu, P., Wen, L., Du, D., Bian, X., Hu, Q., & Ling, H. (2020) · arXiv (Cornell University) · Cited by 145 · see citing works

DOTA: A Large-scale Dataset for Object Detection in Aerial ImagesPreprint

Xia, G.-S., Bai, X., Ding, J., Zhu, Z., Belongie, S., Luo, J., Datcu, M. P., Pelillo, M., & Zhang, L. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1711.10398 · Cited by 138 · see citing works

VisDrone-DET2018: The Vision Meets Drone Object Detection in Image Challenge Results

Zhu, P., Wen, L., Du, D., Bian, X., Ling, H., Hu, Q., Nie, Q., Cheng, H., Liu, C., Liu, X., et al. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-11021-5_27 · Cited by 142 · see citing works

Urban Traffic Density Estimation Based on Ultrahigh-Resolution UAV Video and Deep Neural Network

Zhu, J., Sun, K., Jia, S., Li, Q., Hou, X., Lin, W., Liu, B., & Qiu, G. (2018) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2018.2879368 · Cited by 127 · see citing works

Improvements to Context Based Self-Supervised Learning

Mundhenk, T. N., Ho, D., & Chen, B. Y. (2018) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2018.00973 · Cited by 122 · see citing works

A survey of crowd counting and density estimation based on convolutional neural network

Fan, Z., Zhang, H., Zhang, Z., Lu, G., Zhang, Y., & Wang, Y. (2021) · Neurocomputing · doi:10.1016/j.neucom.2021.02.103 · Cited by 122 · see citing works

Crystal nucleation in metallic alloys using x-ray radiography and machine learning

Arteta, C., Lempitsky, V., Žák, J., Lui, A., Noble, J. A., & Zisserman, A. (2018) · Science Advances · doi:10.1126/sciadv.aar4004 · Cited by 120 · see citing works

Multisized Object Detection Using Spaceborne Optical Imagery

Haroon, M. B., Shahzad, M., & Fraz, M. M. (2020) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2020.3000317 · Cited by 119 · see citing works

Channel Attention Networks

Bastidas, A., & Tang, H. (2019) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw.2019.00117 · Cited by 117 · see citing works

Methods for Small, Weak Object Detection in Optical High-Resolution Remote Sensing Images: A survey of advances and challenges

Han, W., Chen, J., Wang, L., Feng, R., Li, F., Wu, L., Tian, T., & Yan, J. (2021) · IEEE Geoscience and Remote Sensing Magazine · doi:10.1109/mgrs.2020.3041450 · Cited by 115 · see citing works

Artificial Intelligence-Enabled Traffic Monitoring System

Mandal, V., Mussah, A. R., Jin, P., & Adu-Gyamfi, Y. O. (2020) · Sustainability · doi:10.3390/su12219177 · Cited by 113 · see citing works

Fine-tuned YOLOv5 for real-time vehicle detection in UAV imagery: Architectural improvements and performance boost

Hamzenejadi, M. H., & Mohseni, H. (2023) · Expert Systems with Applications · doi:10.1016/j.eswa.2023.120845 · Cited by 112 · see citing works

Recent Advances in Object Detection in the Age of Deep Convolutional Neural NetworksPreprint

Agarwal, S., du Terrail, J. O., & Jurie, F. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1809.03193 · Cited by 133 · see citing works

A Survey of Deep Learning-Based Object Detection Methods and Datasets for Overhead Imagery

Kang, J., Tariq, S., Oh, H., & Woo, S. S. (2022) · IEEE Access · doi:10.1109/access.2022.3149052 · Cited by 112 · see citing works

Convolutional-Neural Network-Based Image Crowd Counting: Review, Categorization, Analysis, and Performance Evaluation

Ilyas, N., Shahzad, A., & Kim, K. (2019) · Sensors · doi:10.3390/s20010043 · Cited by 101 · see citing works

Computer Vision – ECCV 2016

Welling, M., Sebe, N., Matas, J., & Leibe, B. (2016) · Lecture notes in computer science · doi:10.1007/978-3-319-46487-9 · Cited by 99 · see citing works

Super-resolution reconstruction of infrared images based on a convolutional neural network with skip connections

Zou, Y. L., Zhang, L., Liu, C., Wang, B., Hu, Y. J., & Chen, Q. (2021) · Optics and Lasers in Engineering · doi:10.1016/j.optlaseng.2021.106717 · Cited by 100 · see citing works

Towards the next generation of Geospatial Artificial Intelligence

Mai, G., Xie, Y., Jia, X., Lao, N., Rao, J., Zhu, Q., Liu, Z., Chiang, Y., & Jiao, J. (2025) · International Journal of Applied Earth Observation and Geoinformation · doi:10.1016/j.jag.2025.104368 · Cited by 126 · see citing works

SpaceNet MVOI: A Multi-View Overhead Imagery Dataset

Weir, N., Lindenbaum, D., Bastidas, A., Etten, A., Kumar, V., Mcpherson, S., Shermeyer, J., & Tang, H. (2019) · 2019 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00108 · Cited by 95 · see citing works

Vehicle Detection from Aerial Images Using Deep Learning: A Comparative Study

Ammar, A., Koubâa, A., Ahmed, M., Saad, A., & Benjdira, B. (2021) · Electronics · doi:10.3390/electronics10070820 · Cited by 94 · see citing works

Physical Adversarial Attacks on an Aerial Imagery Object Detector

Du, A., Chen, B., Chin, T.-J., Law, Y. W., Sasdelli, M., Rajasegaran, R., & Campbell, D. T. (2022) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv51458.2022.00385 · Cited by 94 · see citing works

Object recognition datasets and challenges: A review

Salari, A., Djavadifar, A., Liu, X., & Najjaran, H. (2022) · Neurocomputing · doi:10.1016/j.neucom.2022.01.022 · Cited by 97 · see citing works

UAVs meet LLMs: Overviews and perspectives towards agentic low-altitude mobility

Tian, Y., Lin, F., Li, Y., Zhang, T., Zhang, Q., Fu, X., Huang, J., Dai, X., Wang, Y., Tian, C., et al. (2025) · Information Fusion · doi:10.1016/j.inffus.2025.103158 · Cited by 102 · see citing works

Oriented Bounding Boxes for Small and Freely Rotated Objects

Zand, M., Etemad, A., & Greenspan, M. (2021) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2021.3076050 · Cited by 89 · see citing works

Counting From Sky: A Large-Scale Data Set for Remote Sensing Object Counting and a Benchmark Method

Gao, G., Liu, Q., & Wang, Y. (2020) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2020.3020555 · Cited by 85 · see citing works

Object Detection and Tracking Algorithms for Vehicle Counting: A Comparative Analysis

Mandal, V., & Adu-Gyamfi, Y. O. (2020) · Journal of Big Data Analytics in Transportation · doi:10.1007/s42421-020-00025-w · Cited by 82 · see citing works

Leveraging Synthetic Data in Object Detection on Unmanned Aerial Vehicles

Kiefer, B., Ott, D., & Zell, A. (2022) · Proceedings - International Conference on Pattern Recognition/Proceedings/International Conference on Pattern Recognition · doi:10.1109/icpr56361.2022.9956710 · Cited by 89 · see citing works

STEERER: Resolving Scale Variations for Counting and Localization via Selective Inheritance Learning

Han, T., Bai, L., Liu, L., & Ouyang, W. (2023) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51070.2023.01997 · Cited by 76 · see citing works

Satellite Imagery Multiscale Rapid Detection with Windowed Networks

Etten, A. V. (2018) · IEEE Workshop/Winter Conference on Applications of Computer Vision · doi:10.1109/wacv.2019.00083 · Cited by 65 · see citing works

Zero-Shot Object Counting

Xu, J., Le, H., Nguyen, V., Ranjan, V., & Samaras, D. (2023) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52729.2023.01492 · Cited by 74 · see citing works

Advancing horizons in remote sensing: a comprehensive survey of deep learning models and applications in image classification and beyond

Paheding, S., Saleem, A. I., Siddiqui, M. F. H., Rawashdeh, N. A., Essa, A. E., & Reyes, A. A. (2024) · Neural Computing and Applications · doi:10.1007/s00521-024-10165-7 · Cited by 75 · see citing works

A Method for Vehicle Detection in High-Resolution Satellite Images that Uses a Region-Based Object Detector and Unsupervised Domain Adaptation

Koga, Y., Miyazaki, H., & Shibasaki, R. (2020) · Remote Sensing · doi:10.3390/rs12030575 · Cited by 73 · see citing works

Divide and Count: Generic Object Counting by Image Divisions

Stahl, T., Pintea, S. L., & van Gemert, J. C. (2018) · IEEE Transactions on Image Processing · doi:10.1109/tip.2018.2875353 · Cited by 72 · see citing works

VAID: An Aerial Image Dataset for Vehicle Detection and Classification

Lin, H.-Y., Tu, K.-C., & Li, C.-Y. (2020) · IEEE Access · doi:10.1109/access.2020.3040290 · Cited by 70 · see citing works

NWPU-MOC: A Benchmark for Fine-Grained Multicategory Object Counting in Aerial Images

Gao, J., Zhao, L., & Li, X. (2024) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2024.3356492 · Cited by 69 · see citing works

A CNN-Based Method of Vehicle Detection from Aerial Images Using Hard Example Mining

Koga, Y., Miyazaki, H., & Shibasaki, R. (2018) · Remote Sensing · doi:10.3390/rs10010124 · Cited by 68 · see citing works

Machine Learning Analysis and Discovery of Zero-Dimensional ns 2 Metal Halides toward Enhanced Photoluminescence Quantum Yield

Моlokeev, М. S., Su, B., Aleksandrovsky, A. S., Golovnev, N. N., Plyaskin, M. E., & Xia, Z. (2022) · Chemistry of Materials · doi:10.1021/acs.chemmater.1c02725 · Cited by 68 · see citing works

Aerial Images Processing for Car Detection using Convolutional Neural Networks: Comparison between Faster R-CNN and YoloV3Preprint

Ammar, A., Koubâa, A., Ahmed, M., & Saad, A. (2019) · Preprints.org · doi:10.20944/preprints201910.0195.v1 · Cited by 64 · see citing works

Few-Shot Object Counting and Detection

Nguyen, T. H., Pham, C., Nguyen, K., & Hoai, M. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-20044-1_20 · Cited by 61 · see citing works

Bidirectional Convolutional LSTM Neural Network for Remote Sensing Image Super-Resolution

Chang, Y., & Luo, B. (2019) · Remote Sensing · doi:10.3390/rs11202333 · Cited by 61 · see citing works

Learning Multi-Level Density Maps for Crowd Counting

Jiang, X., Zhang, L., Lv, P., Guo, Y., Zhu, R., Li, Y., Pang, Y., Li, X., Zhou, B., & Xu, M. (2019) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2019.2933920 · Cited by 59 · see citing works

Deep Learning-Based Quantification of Pulmonary Hemosiderophages in Cytology Slides

Marzahl, C., Aubreville, M., Bertram, C. A., Stayt, J., Jasensky, A.-K., Bartenschlager, F., Fragoso-Garcia, M., Barton, A. K., Elsemann, S., Jabari, S., et al. (2020) · Scientific Reports · doi:10.1038/s41598-020-65958-2 · Cited by 58 · see citing works

Guided attention network for object detection and counting on drones

YuanQiang, C., Du, D., Zhang, L., Wen, L., Wang, W., Wu, Y., & Lyu, S. (2020) · Proceedings of the 28th ACM International Conference on Multimedia · doi:10.1145/3394171.3413816 · Cited by 58 · see citing works

TorchGeo

Stewart, A. J., Robinson, C., Corley, I., Ortiz, A., Ferres, J. L., & Banerjee, A. (2022) · ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems (SIGSPATIAL GIS) · doi:10.1145/3557915.3560953 · Cited by 62 · see citing works

PSGCNet: A Pyramidal Scale and Global Context Guided Network for Dense Object Counting in Remote-Sensing Images

Gao, G., Liu, Q., Hu, Z., Li, L., Wen, Q., & Wang, Y. (2022) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2022.3153946 · Cited by 56 · see citing works

A systematic review on computer vision-based parking lot management applied on public datasets

de Almeida, P. R. L., Alves, J. H., Parpinelli, R. S., & Barddal, J. P. (2022) · Expert Systems with Applications · doi:10.1016/j.eswa.2022.116731 · Cited by 61 · see citing works

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Ðukić, N., Lukežič, A., Zavrtanik, V., & Kristan, M. (2023) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51070.2023.01730 · Cited by 69 · see citing works

Deep Learning-Based Quantification of Pulmonary Hemosiderophages in Cytology Slides

Marzahl, C., Aubreville, M., Bertram, C., Stayt, J., Jasensky, A., Bartenschlager, F., Fragoso, M., Barton, A. K., Elsemann, S., & Jabari, S. (2020) · Refubium (Universitätsbibliothek der Freien Universität Berlin) · doi:10.17169/refubium-27975 · Cited by 54 · see citing works

Livestock detection in aerial images using a fully convolutional network

Han, L., Tao, P., & Martin, R. R. (2019) · Computational Visual Media · doi:10.1007/s41095-019-0132-5 · Cited by 53 · see citing works

EAGLE: Large-Scale Vehicle Detection Dataset in Real-World Scenarios using Aerial Imagery

Azimi, S. M., Bahmanyar, R., Henry, C., & Kurz, F. (2021) · Proceedings - International Conference on Pattern Recognition/Proceedings/International Conference on Pattern Recognition · doi:10.1109/icpr48806.2021.9412353 · Cited by 53 · see citing works

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Yi, J., Shen, Z., Chen, F., Zhao, Y., Xiao, S., & Zhou, W. (2023) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2023.3238185 · Cited by 52 · see citing works

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Mundhenk, T. N., Chen, B. Y., & Friedland, G. H. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1911.11293 · Cited by 87 · see citing works

Counting plants using deep learning

Ribera, J., Chen, Y., Boomsma, C. R., & Delp, E. J. (2017) · IEEE Global Conference on Signal and Information Processing · doi:10.1109/globalsip.2017.8309180 · Cited by 50 · see citing works

Invariant Feature-Based Darknet Architecture for Moving Object Classification

Vasavi, S., Priyadarshini, N., & Harshavaradhan, K. (2020) · IEEE Sensors Journal · doi:10.1109/jsen.2020.3007883 · Cited by 49 · see citing works

Gaussian similarity-based adaptive dynamic label assignment for tiny object detection

Fu, R., Chen, C., Yan, S. Z., Heidari, A. A., Wang, X., Escorcia‐Gutierrez, J., Mansour, R. F., & Chen, H. (2023) · Neurocomputing · doi:10.1016/j.neucom.2023.126285 · Cited by 48 · see citing works

Efficient image dataset classification difficulty estimation for predicting deep-learning accuracy

Scheidegger, F., Istrate, R., Mariani, G., Luca Benini, C., Bekas, C., & Malossi, C. (2020) · The Visual Computer · doi:10.1007/s00371-020-01922-5 · Cited by 47 · see citing works

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Xue, F., Jin, W., Qiu, S., & Yang, J. (2021) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2021.3128989 · Cited by 46 · see citing works

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Li, J., Tian, P., Song, R., Xu, H., Li, Y., & Du, Q. (2024) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2024.3360456 · Cited by 44 · see citing works

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Kang, S., Moon, W., Kim, E., & Heo, J. (2024) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v38i3.28050 · Cited by 44 · see citing works

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Dryden, N., Maruyama, N., Benson, T., Moon, T., Snir, M., & Essen, B. V. (2019) · IEEE International Parallel and Distributed Processing Symposium (IPDPS) · doi:10.1109/ipdps.2019.00031 · Cited by 44 · see citing works

Weakly supervised object detection with 2D and 3D regression neural networks

Dubost, F., Adams, H. H. H., Yilmaz, P., Bortsova, G., van Tulder, G., Ikram, M. A., Niessen, W. J., Vernooij, M. W., & de Bruijne, M. (2020) · Medical Image Analysis · doi:10.1016/j.media.2020.101767 · Cited by 44 · see citing works

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Huang, Z., Dai, M., Zhang, Y., Zhang, J., & Shan, H. (2024) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52733.2024.01615 · Cited by 44 · see citing works

Counting Vehicles with Deep Learning in Onboard UAV Imagery

Amato, G. M., Ciampi, L., Falchi, F., & Gennaro, C. (2019) · IEEE Symposium on Computers and Communications (ISCC) · doi:10.1109/iscc47284.2019.8969620 · Cited by 41 · see citing works

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Azimi, S. M. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-11012-3_7 · Cited by 39 · see citing works

Scale Region Recognition Network for Object Counting in Intelligent Transportation System

Guo, X., Gao, M., Zhai, W., Li, Q., & Jeon, G. (2023) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2023.3296571 · Cited by 38 · see citing works

An accurate car counting in aerial images based on convolutional neural networks

Kılıç, E., & Öztürk, S. (2021) · Journal of Ambient Intelligence and Humanized Computing · doi:10.1007/s12652-021-03377-5 · Cited by 33 · see citing works

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Raza, A., Abdullah, J., Khan, R. U., Hipiny, I., & Ujir, H. B. (2025) · doi:10.1109/iceei68459.2025.11330380

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Wei, X.-Y., Zhang, L., Ma, H., & Zhang, X. (2024) · Transportation Research Part C Emerging Technologies · doi:10.1016/j.trc.2024.104608 · Cited by 6 · see citing works

Invariant Feature-Based Darknet Architecture for Moving Object Classification

Vasavi, S., Priyadarshini, N., & Harshavaradhan, K. (2020) · IEEE Sensors Journal · doi:10.1109/jsen.2020.3007883 · Cited by 49 · see citing works

Deep Neural Networks for Object Enumeration

Xu, Z., & Salloum, M. (2018) · IEEE International Conference on Big Data (BigData) · doi:10.1109/bigdata.2018.8622191