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

Deep Residual Learning for Image Recognition

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

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Going deeper with convolutions

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

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

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Deep Residual Learning for Image Recognition

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Online Object Tracking: A Benchmark

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

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Correcting Imprecise Object Locations for Training Object Detectors in Remote Sensing Applications

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Object Counting with Small Datasets of Large Images.Preprint

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A Comparison of Super-Resolution and Nearest Neighbors Interpolation Applied to Object Detection on Satellite DataPreprint

Koester, E., & Sahin, C. S. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1907.05283 · Cited by 14

Drone Based RGBT Vehicle Detection and Counting: A ChallengePreprint

Zhu, P., Sun, Y., Wen, L., Feng, Y., & Hu, Q. (2020) · arXiv (Cornell University) · Cited by 10

A Deep-Learning Approach to Detect and Classify Heavy-Duty Trucks in Satellite Images

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A Lightweight Object Counting Network Based on Density Map Knowledge Distillation

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Object detection in aerial images using DOTA dataset: A survey

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Small aerial object detection through GAN-integrated feature pyramid networks

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Evaluation of Spatio-Temporal Small Object Detection in Real-World Adverse Weather Conditions

van Lier, M., Leeuwen, M. V., van Manen, B., Kampmeijer, L., & Boehrer, N. (2025) · IEEE/CVF Winter Conference on Applications of Computer Vision Workshops (WACVW) · doi:10.1109/wacvw65960.2025.00094 · Cited by 10

Vehicle Detection in Aerial Imaginary using a Miniature CNN Architecture

Öztürk, M., & Çavuş, E. (2021) · International Conference on Innovations in Intelligent Systems and Applications (INISTA) · doi:10.1109/inista52262.2021.9548348 · Cited by 9

Vehicle object counting network based on feature pyramid split attention mechanism

Liu, M., Wang, Y., Hu, Y., & Huang, X. (2023) · The Visual Computer · doi:10.1007/s00371-023-02808-y · Cited by 9

Deep Learning Models to Count Buildings in High-Resolution Overhead Images

Lobry, S., & Tuia, D. (2019) · Joint Urban Remote Sensing Event (JURSE) · doi:10.1109/jurse.2019.8809058 · Cited by 9

Road Characteristics Detection Based on Joint Convolutional Neural Networks with Adaptive Squares

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A new comparison framework to survey neural networks‐based vehicle detection and classification approaches

Hashemi, S., Emami, H., & Sangar, A. B. (2021) · International Journal of Communication Systems · doi:10.1002/dac.4928 · Cited by 9

A Novel Dense Generative Net Based on Satellite Remote Sensing Images for Vehicle Classification Under Foggy Weather Conditions

Yuan, J., Liu, T., Xia, H., & Zou, X. (2023) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2023.3336546 · Cited by 9

On the Impact of Perceptual Compression on Deep Learning

Friedland, G. H., Jia, R., Wang, J., Li, B., & Mundhenk, N. (2020) · IEEE International Conference on Multimedia Information Processing and Retrieval (MIPR) · doi:10.1109/mipr49039.2020.00052 · Cited by 9

BlueFog: Make Decentralized Algorithms Practical for Optimization and Deep LearningPreprint

Ying, B., Yuan, K., Hu, H., Chen, Y., & Yin, W. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2111.04287 · Cited by 28

VME: A Satellite Imagery Dataset and Benchmark for Detecting Vehicles in the Middle East and Beyond

Emadi, N. A., Weber, I. G., Yang, Y., & Ofli, F. (2025) · Scientific Data · doi:10.1038/s41597-025-04567-y · Cited by 11

The UAV Benchmark: Compact Detection of Vehicles in Urban Scenarios

Lv, H., Zheng, X., Xie, X. H., Chen, X., & Xiong, H. (2024) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2024.3443268 · Cited by 8

CODrone: A Comprehensive Oriented Object Detection Benchmark for UAV

Ye, K., Tang, H., Liu, B., Dai, P., & Cao, L. (2026) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2026.3677376 · Cited by 21

Deep learning with satellite imagery to enhance environmental enforcement

Handan‐Nader, C., Ho, D. E., & Liu, L. Y. (2021) · Elsevier eBooks · doi:10.1016/b978-0-12-817976-5.00011-5 · Cited by 8

ICPR2018 Contest on Object Detection in Aerial Images (ODAI-18)

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Multiple-step Sampling for Dense Object Detection and Counting

Deng, Z., & Yang, C. (2021) · Proceedings - International Conference on Pattern Recognition/Proceedings/International Conference on Pattern Recognition · doi:10.1109/icpr48806.2021.9412449 · Cited by 9

Privacy preserving Neural Network Inference on Encrypted Data with GPUsPreprint

Takabi, D., Podschwadt, R., Druce, J., Wu, C., & Procopio, K. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1911.11377 · Cited by 15

VETRA: A Dataset for Vehicle Tracking in Aerial Imagery – New Challenges for Multi-Object Tracking

Hellekes, J., Muhlhaus, M. S., Bahmanyar, R., Azimi, S. M., & Kurz, F. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-73013-9_4 · Cited by 7

Multitask assessment of roads and vehicles network (MARVN)

Chen, G., Yang, F., Yi, M., Cai, Y., Blasch, E., Ling, H., Sullivan, N., & Sheaff, C. (2018) · Sensors and Systems for Space Applications · doi:10.1117/12.2305972 · Cited by 7

Generation of Synthetic Elevation Models and Realistic Surface Images of River Deltas and Coastal Terrains Using cGANs

Valencia-Rosado, L. O., Guzman-Zavaleta, Z. J., & Starostenko, O. (2020) · IEEE Access · doi:10.1109/access.2020.3048083 · Cited by 8

CCA: Exploring the Possibility of Contextual Camouflage Attack on Object Detection

Hu, S., Zhang, Y., Laha, S., Sharma, A. K., & Foroosh, H. (2021) · Proceedings - International Conference on Pattern Recognition/Proceedings/International Conference on Pattern Recognition · doi:10.1109/icpr48806.2021.9413194 · Cited by 7

QE-DAL: A quantum image feature extraction with dense distribution-aware learning framework for object counting and localization

Hu, R., Tang, Z., & Yang, R. (2023) · Applied Soft Computing · doi:10.1016/j.asoc.2023.110149 · Cited by 7

Investigating the Resolution-Performance Trade-off of Object Detection Models in Support of the Sustainable Development Goals

Clark, C. N., Bedada, A., Huff, B., Lita, B., Newbury, A. B., & Pacifici, F. (2023) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2023.3284309 · Cited by 7

A comprehensive review of optical remote-sensing image object detection datasets

Yiqin, Y., Li, L., Yao, X., Li, L., Feng, X., Cheng, G., & Han, J. (2023) · National Remote Sensing Bulletin · doi:10.11834/jrs.20233457 · Cited by 7

Efficient and Switchable CNN for Crowd Counting Based on Embedded Terminal

Chen, J., Zhang, Q., Zheng, W., & Xie, X. (2019) · IEEE Access · doi:10.1109/access.2019.2910458 · Cited by 7

Crowd Density Estimation by Using Attention Based Capsule Network and Multi-Column CNN

Kızrak, M. A., & Bölat, B. (2021) · IEEE Access · doi:10.1109/access.2021.3081529 · Cited by 7

Pyramid Information Distillation Attention Network for Super-Resolution Reconstruction of Remote Sensing Images

Huang, B., Guo, Z., Wu, L., He, B., Li, X., & Lin, Y. (2021) · Remote Sensing · doi:10.3390/rs13245143 · Cited by 7

Class-agnostic counting and localization with feature augmentation and scale-adaptive aggregation

Zhai, C., Du, Y., Qu, H., Wang, T., Zhang, F., Fu, M., & Chen, W. (2024) · Knowledge-Based Systems · doi:10.1016/j.knosys.2024.112104 · Cited by 6

A Review of Object Detection in Aerial Images by Deep Learning Approaches

Vijai, A., Padmavathi, S., & Venkataraman, D. (2026) · SN Computer Science · doi:10.1007/s42979-025-04685-9 · Cited by 6

Convolutional neural network for human crowd analysis: a review

Amrish, A., Arya, S., & Kumar, S. (2023) · Multimedia Tools and Applications · doi:10.1007/s11042-023-16841-5 · Cited by 6

Effect of Preprocessing on Performance of Neural Networks for Microscopy Image Classification

Uka, A., Polisi, X., Barthès, J., Halili, A. N., Skuka, F., & Vrana, N. E. (2020) · International Conference on Computing, Electronics & Communications Engineering (iCCECE) · doi:10.1109/iccece49321.2020.9231071 · Cited by 7

Efficient Fruit and Vegetable Classification and Counting for Retail Applications Using Deep Learning

Bogomasov, K., & Conrad, S. (2021) · International Conference on Advances in Artificial Intelligence (ICAAI) · doi:10.1145/3505711.3505720 · Cited by 6

Categorization of Crowd Varieties using Deep Concurrent Convolution Neural Network

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ArtifiVe-Potsdam: A Benchmark for Learning with Artificial Objects for Improved Aerial Vehicle Detection

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DMLViT: Dynamic Multi-Scale Local Vision Transformer for Object Counting in Congested Traffic Scenes

Lin, C., & Hu, X. (2025) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2025.3576327 · Cited by 5

Wafer Pattern Counting, Detection and Classification Based on Encoder-Decoder CNN Structure

Lin, Y. (2022) · IEEE International Conference on Electro Information Technology · doi:10.1109/eit53891.2022.9813870 · Cited by 5

Learning to Count Mosquitoes for the Sterile Insect Technique

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Self-Supervised Deep Learning for Vehicle Detection in High-Resolution Satellite Imagery

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An Adaptive Multi-Scale Network Based on Depth Information for Crowd Counting

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Counting and Locating Anything: Class-agnostic Few-shot Object Counting and Localization

Zhang, Y., Zhang, H., & Zhao, Q. (2023) · IEEE International Conference on Multimedia and Expo (ICME) · doi:10.1109/icme55011.2023.00496 · Cited by 5

Detection of different types of vehicles from aerial imagery.

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Learning to Count Objects with Few Exemplar AnnotationsPreprint

Wang, J., Xiao, R., Guo, Y., & Zhang, L. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1905.07898 · Cited by 5

AirSpatialBot: A Spatially Aware Aerial Agent for Fine-Grained Vehicle Attribute Recognition and Retrieval

Zhou, Y., Ding, R., Yang, X., Jiang, X., & Liu, X. (2025) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2025.3570895 · Cited by 5

Privacy‐Preserving Crowd Counting via Quantum‐Enhanced Federated Learning

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WHU-RS19 ABZSL: An Attribute-Based Dataset for Remote Sensing Image Understanding

Balestra, M., Paolanti, M., & Pierdicca, R. (2025) · Remote Sensing · doi:10.3390/rs17142384 · Cited by 5

Distortion-Adaptive Grape Bunch Counting for Omnidirectional Images

Akai, R., Utsumi, Y., Miwa, Y., Iwamura, M., & Kise, K. (2021) · Proceedings - International Conference on Pattern Recognition/Proceedings/International Conference on Pattern Recognition · doi:10.1109/icpr48806.2021.9412659 · Cited by 4

Adapting Vehicle Detector to Target Domain by Adversarial Prediction Alignment

Koga, Y., Miyazaki, H., & Shibasaki, R. (2021) · IEEE International Geoscience and Remote Sensing Symposium (IGARSS) · doi:10.1109/igarss47720.2021.9554416 · Cited by 4

Comparative Analysis of Deep Learning based Vehicle Detection Approaches

Singhal, N., & Prasad, L. (2023) · INTERNATIONAL JOURNAL OF NEXT-GENERATION COMPUTING · doi:10.47164/ijngc.v14i2.976 · Cited by 4

Vehicle Detection and Attribution from a Multi-Sensor Dataset Using a Rule-Based Approach Combined with Data Fusion

Bowman, L. A., Narayanan, R. M., Kane, T. J., Bradley, E. S., & Baran, M. S. (2023) · Sensors · doi:10.3390/s23218811 · Cited by 4

Few-Shot Object Counting With Dynamic Similarity-Aware in Latent Space

He, J., Liu, B., Cao, F., Xu, J., & Xiao, Y. (2024) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2024.3350383 · Cited by 4

Computer Vision-based Approaches, Datasets, and Applications of Smart Parking Systems: A Review

Hadi, R. A., George, L. E., & Ahmed, Z. J. (2024) · Iraqi Journal of Science · doi:10.24996/ijs.2024.65.2.32 · Cited by 4

Estimating the Spatial Resolution of Very High-Resolution Overhead Imagery

Liang, H., & Newsam, S. D. (2019) · ACM SIGSPATIAL International Workshop on AI for Geographic Knowledge Discovery (GeoAI) · doi:10.1145/3356471.3365241 · Cited by 4

Open Challenges for Crowd Density Estimation

Alshaya, S. A. (2020) · International Journal of Advanced Computer Science and Applications · doi:10.14569/ijacsa.2020.0110123 · Cited by 4

Enhanced Super-Resolution via Squeeze-and-Residual-Excitation in Aerial Imagery

Khan, B., Fraz, M. M., & Mumtaz, A. (2021) · International Conference on Frontiers of Information Technology (FIT) · doi:10.1109/fit53504.2021.00014 · Cited by 4

Traffic Density Estimation by Distributed Proxy Model Learning for Internet of Vehicle

Li, Q., Cheng, J., Gao, M., Chen, J., & Jeon, G. (2025) · IEEE Internet of Things Journal · doi:10.1109/jiot.2025.3542534 · Cited by 3

MGSANet: A Multiscale Graph Spatial Alignment Network for Weakly Aligned RGB-Thermal Object Detection

Wang, Q., Sun, Y., Shen, T., Al-Antary, M. T., Alasmary, H., & Waqas, M. (2025) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2025.3647051 · Cited by 4

DFGOVD: A New Dataset and Benchmark for Drone Fine-Grained Oriented Vehicle Detection in the Wild

Zheng, J., Feng, Z., Xing, M., Su, Y. R., Peng, W., & Zhang, Y. (2026) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2026.3660958 · Cited by 5

Learning to Count Without Annotations

Knobel, L., Han, T., & Asano, Y. M. (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/cvpr52733.2024.02163 · Cited by 5

Efficient vehicular counting via privacy-aware aggregation network

Cheng, J.-A., Li, Q., Chen, J., & Gao, M. (2025) · Measurement Science and Technology · doi:10.1088/1361-6501/ada6f1 · Cited by 3

CREAM: Few-shot Object Counting with Cross REfinement and Adaptive density Map

Xu, Y., Li, M., Ye, Q., Wang, S., Li, L., & Zhang, H. (2025) · Image and Vision Computing · doi:10.1016/j.imavis.2025.105632 · Cited by 3

Vehicle Counting: Survey and Experiments

La, H.-P., Ha, M.-T., Nguyen, H.-L., & Nguyen, M.-T. (2020) · NAFOSTED Conference on Information and Computer Science (NICS) · doi:10.1109/nics51282.2020.9335840 · Cited by 4

Guided Generative Models using Weak Supervision for Detecting Object Spatial Arrangement in Overhead Images

Duan, W., Chiang, Y., Leyk, S., Uhl, J. H., & Knoblock, C. A. (2021) · IEEE International Conference on Big Data (BigData) · doi:10.1109/bigdata52589.2021.9671875 · Cited by 3

YOLO-CFM: Improved YOLOv5 for Vehicle Detection in Drone-captured Infrared Images

Zhang, Y., Zhang, Y., Gu, F., Chen, M., & Zhu, H. (2022) · International Conference on Big-data Service and Intelligent Computation (BDSIC) · doi:10.1145/3578339.3578340 · Cited by 3

Spatio-Temporal Alignment and Track-To-Velocity Module for Tropical Cyclone Forecast

Geng, X., Liu, Z., & Shi, Z. (2023) · Remote Sensing · doi:10.3390/rs15204938 · Cited by 3

Super-Resolution Learning Strategy Based on Expert Knowledge Supervision

Ren, Z., He, L., & Zhu, P. (2024) · Remote Sensing · doi:10.3390/rs16162888 · Cited by 3

Class-Agnostic Object Counting with Text-to-Image Diffusion Model

Hui, X., Wu, Q., Rahmani, H., & Liu, J. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-72890-7_1 · Cited by 3

EFSOD: Enhanced Feature based Small Object Detection Network in Remote Sensing Images

Yi, J., Liu, Y., Li, Y., & Xie, W. (2025) · Chinese journal of information fusion. · doi:10.62762/cjif.2025.845143 · Cited by 3

Few-Shot Object Counting with frequency attention and multi-perception head

Ma, G., Xingquan Zhu, A., Tian, Z., Ye, Y., & Zhu, Z. (2025) · Neurocomputing · doi:10.1016/j.neucom.2025.130598 · Cited by 3

NF-Net: Near and Far Network for Crowd Counting

Zhang, P., Lei, W., Zhao, X. S., Dong, L., & Lin, Z. (2023) · International Conference on Computer and Communication Systems (ICCCS) · doi:10.1109/icccs57501.2023.10151219 · Cited by 3

National-Standards- and Deep-Learning-Oriented Raster and Vector Benchmark Dataset (RVBD) for Land-Use/Land-Cover Mapping in the Yangtze River Basin

Zhang, P., Wu, Y., Li, C., Li, R., Yao, H., Zhang, Y., Zhang, G., & Li, D. (2023) · Remote Sensing · doi:10.3390/rs15153907 · Cited by 4

Mining and Analysis of Air Quality Data to Aid Climate Change

Saheer, L. B., Shahawy, M., & Zarrin, J. (2020) · IFIP advances in information and communication technology · doi:10.1007/978-3-030-49190-1_21 · Cited by 3

Watermark Retrieval from 3D Printed Objects via Convolutional Neural NetworksPreprint

Zhang, X., Wang, Q., Breckon, T. P., & Ivrissimtzis, I. P. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1811.07640 · Cited by 3

Artificial intelligence enabled automatic traffic monitoring system

Mandal, V. (2019) · doi:10.32469/10355/72268 · Cited by 3

Airplane Detection Based on Mask Region Convolution Neural NetworkPreprint

Alshaibani, W., Helvaci, M., Shayea, I. A. M., & Mohamad, H. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2108.12817 · Cited by 6

Sampling Equivariant Self-attention Networks for Object Detection in Aerial ImagesPreprint

Yang, G.-Y., Li, X., Martin, R. R., & Hu, S. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2111.03420 · Cited by 3

Ancient character detection based on fine-grained density map

Yue, M., Shi, D., Diao, X., Guo, S., Li, C., & Xu, H. (2025) · npj Heritage Science · doi:10.1038/s40494-025-01791-y · Cited by 2

The Impact of REER Volatility on Economic Activity in Ukraine

Chepyha, B. (2025) · Visnyk of the National Bank of Ukraine · doi:10.26531/vnbu2025.op04 · Cited by 2

Lightweight super-resolution reconstruction architecture of remote sensing images using a residual hierarchical transformer network

Huang, B., Lin, J. F., Chen, Q., Cao, Y., & Wu, L. (2025) · Scientific Reports · doi:10.1038/s41598-025-28042-1 · Cited by 2

Review of Generative Adversarial Networks in Object Detection

Zhou, C., Kong, S., & Sun, J. (2023) · Lecture notes in electrical engineering · doi:10.1007/978-981-99-1256-8_20 · Cited by 2

Attention Enhanced Multi-Scale Feature Map Fusion Few Shot Learning

Feng, X., Han, L., Tao, P., & Jiang, Y. (2023) · Asia-Pacific Conference on Communications Technology and Computer Science (ACCTCS) · doi:10.1109/acctcs58815.2023.00096 · Cited by 2

Few-shot Class-agnostic Counting with Occlusion Augmentation and Localization

Su, Y., Wang, Y., Yao, L., & Chau, L. (2024) · IEEE International Symposium on Circuits and Systems proceedings · doi:10.1109/iscas58744.2024.10558069 · Cited by 2

An unmanned aerial system benchmark object detection dataset for deep learning in outfall surveys

Wu, C., Huang, Y., Yang, H., Yao, L., Liu, Y., & Chen, Z. (2024) · International Journal of Digital Earth · doi:10.1080/17538947.2024.2390443 · Cited by 2

Multi-scale information distillation attention network for super-resolution reconstruction of remote sensing images

Huang, B., Wu, L., Cao, Y., & Zhong, M. (2025) · Journal of Measurements in Engineering · doi:10.21595/jme.2024.24351 · Cited by 2

Vehicle-BD: A Benchmark Dataset of Bangladeshi Local Vehicles

Rahman, M. S., Hassan, M. Z., & Ibtisum, S. (2025) · Lecture notes in networks and systems · doi:10.1007/978-3-031-78949-6_14 · Cited by 2

LEARNING WITH REAL-WORLD AND ARTIFICIAL DATA FOR IMPROVED VEHICLE DETECTION IN AERIAL IMAGERY

Weber, I., Bongartz, J., & Roscher, R. (2020) · ISPRS annals of the photogrammetry, remote sensing and spatial information sciences · doi:10.5194/isprs-annals-v-2-2020-917-2020 · Cited by 2

Ground2sky label transfer for fine-grained aerial car recognition

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UAS-based object tracking via Deep Learning

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