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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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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SAT-CNN: A Small Neural Network for Object Recognition from Satellite Imagery

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Objects Counting in Videos via Deep Learning and Image Processing

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Rapid Detection of Aircrafts in Satellite Imagery based on Deep Neural NetworksPreprint

Tahir, A., Adil, M. Z., & Ali, A. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2104.11677 · Cited by 2

Airplane Type Identification Based on Mask RCNN and Drone ImagesPreprint

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An Algorithm for Counting People using Dense Nets and Feature Fusion

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TinyWT: A Large-Scale Wind Turbine Dataset of Satellite Images for Tiny Object Detection

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MRSNet: Multi-Resolution Scale Feature Fusion-Based Universal Density Counting Network

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Unified ViT-CNN for few-shot object counting

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Few-Shot Counting with Multi-Scale Vision Transformers and Attention Mechanisms

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Enhancing few-shot visual counting with spectral-attention fusion

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Small targets detection in low-resolution remote sensing images based on super-resolution joint optimization

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Object Detection in VHR Optical Remote Sensing Image: A Review

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Enhancing Urban Infrastructure Management with UAV-Based Parking Solutions: A Case Study at UCC

O’Shaughnessy, E., Hao, G., Dai, Z., & Li, Z. (2024) · International Conference on Universal Village (UV) · doi:10.1109/uv63228.2024.11189170 · Cited by 1

Feature Adaptive Selection and Fusion Network for Small Object Detection in Traffic Perception

Ibrahim, A. P. O. H., Zhao, Q., Li, H., & Yu, R. (2025) · Annual Conference of the IEEE Industrial Electronics Society (IECON) · doi:10.1109/iecon58223.2025.11221887 · Cited by 1

KFGOD: A Fine-Grained Object Detection Dataset in KOMPSAT Satellite Imagery

Lee, D., Hong, J. H., Seo, H. W., & Oh, H. (2025) · Remote Sensing · doi:10.3390/rs17223774 · Cited by 1

Counting Beyond Domains: Toward Alignment in Unsupervised Domain Adaptation in Remote Sensing Object Counting

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Neural Network Assessment of Spatial Resolution of Multi-Scale Aerial Photographs of Urban and Natural Landscapes

Zabolotskiy, A. V., & Sobyanin, K. V. (2025) · Doklady Mathematics · doi:10.1134/s1064562425700358 · Cited by 1

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Liu, S., Shao, F., & Xue, J. (2026) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2026.115171 · Cited by 1

Deep Domain Adaptation for Single-Shot Vehicle Detector in Satellite Images

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Towards Efficient Aerial Object Detection: Techniques, Datasets and Challenges

Anwar, H., Javed, K., Rubab, S., & Khan, M. J. (2021) · International Conference on Communication Technologies (ComTech) · doi:10.1109/comtech52583.2021.9616675 · Cited by 1

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

Yu, L. (2022) · Intermountain Engineering, Technology and Computing (IETC) · doi:10.1109/ietc54973.2022.9796856 · Cited by 1

Re-evaluation of the CNN-based state-of-the-art crowd-counting methods with enhancements

Tersek, M., Kljun, M., Peer, P., & Emeršič, Ž. (2022) · Computer Science and Information Systems · doi:10.2298/csis210925023t · Cited by 1

MetaCNN: A New Hybrid Deep Learning Image-based Approach for Vehicle Classification Using Transformer-like Framework

Chen, J., & Luo, R.-K. (2022) · International Conference on Computer Science and Software Engineering · doi:10.1145/3569966.3570099 · Cited by 2

Comparation of Rotational Deep Learning based Methods

Brassai, S. T., Németh, A., Hammas, A., & Sagyebó, I. D. (2023) · International Carpathian Control Conference · doi:10.1109/iccc57093.2023.10178922 · Cited by 1

Tolerating Annotation Displacement in Dense Object Counting via Point Annotation Probability Map

Chen, Y., Yang, J., Chen, B., Du, S., & Hua, G. (2023) · IEEE Transactions on Image Processing · doi:10.1109/tip.2023.3331908 · Cited by 1

Vehicle Detection in Aerial Images: A Survey

Kumar, D., & Sinha, B. (2024) · Studies in autonomic, data-driven and industrial computing · doi:10.1007/978-981-99-5435-3_10 · Cited by 1

Syn2Real Detection in the Sky: Generation and Adaptation of Synthetic Aerial Ship Images

Wu, Y., Guo, W., Tan, Z., Zhao, Y., Zhu, Q., Wu, L., & Guo, Z. (2024) · Applied Sciences · doi:10.3390/app14114558 · Cited by 1

Adaptive learning-enhanced lightweight network for real-time vehicle density estimation

Qin, L.-X., Sun, H.-M., Duan, X., Che, C.-Y., & Jia, R. (2024) · The Visual Computer · doi:10.1007/s00371-024-03572-3 · Cited by 1

Vision Transformers with Cross-Attention Pyramids for Class-Agnostic Counting

Jiban, M. J. H., Mahalanobis, A., & Lobo, N. D. V. (2024) · doi:10.1109/icsip61881.2024.10671540 · Cited by 1

Learning to Count from Pseudo-Labeled Segmentation

Xu, J., Le, H., & Samaras, D. (2025) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv61041.2025.00848 · Cited by 1

Towards trustworthy crowd counting by distillation hierarchical mixture of experts for edge-based cluster computing

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A Framework for Queryable Video Analysis

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Protocol Design Issues for Object Density Estimation and Counting in Remote Sensing

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CiPSI and CiPSINet: a New Dataset and Network for Finding Cars in Panchromatic Satellite Images

Parfitt, B., & Gagadam, T. (2021) · International Conference on Machine Learning and Applications · doi:10.1109/icmla52953.2021.00024 · Cited by 1

Study of Deep Convolutional Neural Network for Vehicle Localization on Blurred Aerial Imagery

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Accurate few-shot object counting with Hough matching feature enhancement

He, Z., Zheng, D., & Wang, H. (2023) · Frontiers in Computational Neuroscience · doi:10.3389/fncom.2023.1145219 · Cited by 1

Szabadtéri parkolás elemzése drónfelvételekkel és gépi látással

Tordai, D., Albert, G., & Munkácsy, A. (2023) · Közlekedés és Mobilitás · doi:10.55348/km.23 · Cited by 1

Simultaneous x, y Pixel Estimation and Feature Extraction for Multiple Small Objects in a Scene: A Description of the ALIEN NetworkPreprint

Zuckerman, S., Klein, T., Boxer, A., Goldman, C., & Lang, B. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1902.05387 · Cited by 1

Watermark retrieval from 3D printed objects via synthetic data trainingPreprint

Zhang, X., Jia, N., & Ivrissimtzis, I. P. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1905.09706 · Cited by 1

Fine-tuning of Fully Convolutional Networks for Vehicle Detection in Satellite Images: Data Augmentation and Hard Examples Mining

Imbert, J. (2019) · KTH Publication Database DiVA (KTH Royal Institute of Technology) · Cited by 1

cofga: A Dataset for Fine Grained Classification of Objects from Aerial ImageryPreprint

Dahan, E., Diskin, T., Amram, A., Moryossef, A., & Koren, O. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2105.12786 · Cited by 1

Automatic Detection of Vehicles in Satellite Images for Economic Monitoring

Hill, C. (2021) · Digital Commons - University of South Florida (University of South Florida) · Cited by 1

Counting Objects in Images using DeepLearning: Methods and Current ChallengesPreprint

D’Alessandro, A., Amiri, A. M., & Hamarneh, G. (2023) · Research Square · doi:10.21203/rs.3.rs-2986682/v1 · Cited by 1

Counting dense objects in remote sensing imagesPreprint

Gao, G., Liu, Q., & Wang, Y. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2002.05928

EAGLE: Large-scale Dataset for Vehicle Detection in Aerial ImageryPreprint

Azimi, S. M., Bahmanyar, R., Henry, C., & Kurz, F. (2020) · arXiv (Cornell University)

AUTOMATIC OBJECT SEGMENTATION TO SUPPORT CRISIS MANAGEMENT OF LARGE-SCALE EVENTS

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An End-to-End Framework for Image Super Resolution and Denoising of SAR Images

Pandey, A. K., Kumar, J. A., & Chattopadhyay, C. (2021) · Transactions on computer systems and networks · doi:10.1007/978-981-16-1681-5_8

A Comparative Study and Analysis on Crowd Density Estimation ApproachesPreprint

Kushwaha, H., & Gupta, S. K. (2021) · SSRN Electronic Journal · doi:10.2139/ssrn.3985903

Learning Selective Assignment Network for Scene-Aware Vehicle Detection

Wang, Z., Li, W., Wu, X. Y., & Sheng, L. (2022) · Proceedings - International Conference on Image Processing · doi:10.1109/icip46576.2022.9897860

Utilization of Artificial Intelligence in Automated Image Analysis

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Generative Adversarial Network and Its Application in Aerial Vehicle Detection and Biometric Identification System

Mostofa, M. (2023) · The Research Repository @ WVU (West Virginia University) · doi:10.33915/etd.11758

Estimating the Impact of COVID-19 on Travel Demand in the Houston Area Using Deep Learning and Satellite Imagery

Pachika, A., Gao, L., Song, L., Lu, P., & Wang, X. (2023) · ASCE International Conference on Transportation and Development (ICTD) · doi:10.1061/9780784484876.038

Al-yousofi: A Novel Benchmark For Multi-vehicle Detection And Tracking

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Detecção de Placas Veiculares em Ambientes Aéreos utilizando Super Resolução por GAN e Equalização de Histograma

Linhares, J. E. B. D. S., Silva, W., Pinagé, F. S., de Melo, W. C., Colares, G. S., Garcia, L. C. D. S., de Jesus, A., Bitar, N. P., Bessa, A. R. R., & Araújo, G. (2023) · Anais de XXXV Simpósio Brasileiro de Telecomunicações e Processamento de Sinais · doi:10.14209/sbrt.2023.1570908005

Attention-Assisted Feature Comparison and Feature Enhancement for Class-Agnostic Counting

Liang, D., Yu, Y., Zhang, D., & Huo, Y. (2023) · Sensors · doi:10.3390/s23229126

Benchmarking and end-to-end considerations for GeoAI-enabled decision-making

Yang, H. L., Dias, P. A., Arndt, J., Wohlgemuth, J., Potnis, A. V., & Lunga, D. (2024) · Elsevier eBooks · doi:10.1016/b978-0-44-319077-3.00011-0

Next-Gen Vehicle Attribute Classification: A Multi-head Deep Learning Approach

Bekbolat, A., Tanveer, M. H., Kashkynbayev, A., & Ali, M. H. (2024) · IEEE International Conference on Computing and Machine Intelligence (ICMI) · doi:10.1109/icmi60790.2024.10585682

Enhanced LOCA model for accurate few-shot counting of warehouse goods

Cui, K., Sun, T., Deng, L. M., & Chen, B. (2024) · Optical Metrology and Inspection for Industrial Applications · doi:10.1117/12.3037248

Supervised Crowd Counting with Combined Losses

Li, X. S., Tian, Y., Li, S., Gao, S., Zhang, Z., Liu, S., & Shi, C. (2024) · International Conference on Computer Science and Blockchain (CCSB) · doi:10.1109/ccsb63463.2024.10735660

Adaptive and Background-Aware Match for Class-Agnostic Counting

Gong, S., Yang, J. J., & Zhang, S. (2025) · IEEE Signal Processing Letters · doi:10.1109/lsp.2025.3546891

Spatial–spectral fusion remote sensing detection with channel-spatial attention and context augmentation and a high-quality benchmark

Shan, J., Zhang, J., Liu, X., Xun, L., & Yan, Q. Y. (2025) · Journal of Electronic Imaging · doi:10.1117/1.jei.34.3.033007

An efficient low-shot class-agnostic counting framework with hybrid encoder and iterative exemplar feature learning

Yang, Q., Liu, B., Tian, Y. T., Shi, Y., Du, X., He, F., & Guo, J. (2025) · PLoS ONE · doi:10.1371/journal.pone.0322360

Selective Scale Context Awareness Network for Object Counting in Intelligent Transportation System

Hu, X., Lin, C., & Yuan, X. (2025) · Journal of Transportation Engineering Part A Systems · doi:10.1061/jtepbs.teeng-8944

Attention-enhanced density regression attention-aware for class-agnostic few-shot object counting with cross-domain generalization

Chen, X., Zhang, Z., & Ren, Y. (2025) · Engineering Research Express · doi:10.1088/2631-8695/adf48b

Iterative learning for few-shot counting: A template-driven approach

Xie, Z., & Wang, G. (2025) · Digital Signal Processing · doi:10.1016/j.dsp.2025.105516

Counting with ease: Class-agnostic counting via one-shot detection across diverse domains

Peng, Z., Guo, B., & Xu, S. (2025) · Neural Networks · doi:10.1016/j.neunet.2025.107961

Structural Consistency Based Multiobject Detection for Satellite Video and Benchmark With Real Scene Simulation Dataset

Zhang, Y., Tang, Y., Liu, F., Chen, Y., & Han, T. (2025) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2025.3603130

AeroYOLO: Efficient Multiscale and Attention-Augmented YOLOv8s for Robust Aerial Object Detection

Zhang, H. (2025) · IEEE Access · doi:10.1109/access.2025.3610617 · Cited by 2

HarmoNet: Balancing Computational Efficiency and Detection Accuracy for Drone-Based Object Recognition

Zhang, H., Liu, C., Wang, Z., Ma, N., Liu, C., Ji, B., Liu, S., Si, H., Xu, L., & Li, Z. (2025) · International Journal of Pattern Recognition and Artificial Intelligence · doi:10.1142/s0218001425500399

A Lightweight Few-Shot Object Counting Method Based on Similarity Matching

Su, B., & Du, B. (2025) · International Congress on Image and Signal Processing (CISP) · doi:10.1109/cisp-bmei68103.2025.11259382

Applying Very High Resolution Satellite Imagery in Nowcasting

Beering, J. L. (2025) · Visnyk of the National Bank of Ukraine · doi:10.26531/vnbu2025.op03

Text-Guided Object Counting via Residual-Gated Shuffle Attention and Frequency Refinement

Qian, C., Mao, Y., Cao, J., Zhang, R., Zhu, P., & Sang, J. (2026) · Lecture notes in computer science · doi:10.1007/978-981-95-5755-4_24

FReID: advancing small-target object detection with feature reintegration and distribution

Miao, Z., Zhang, Q., He, M., Li, Y., Bi, X., Wang, J., & Hou, Q. (2026) · The Journal of Supercomputing · doi:10.1007/s11227-025-08143-4

Exploring Scale Shift in Crowd Localization under the Context of Domain Generalization

Wang, J., Shang, L., Liu, Z., Lu, W., Hu, X., Hu, Z., Wang, J., & Wang, S. (2026) · International Journal of Computer Vision · doi:10.1007/s11263-025-02637-7

DroneDetect: Multiscale Feature Fusion and Attention-Driven Architecture for UAV Object Detection

Zhang, H. (2026) · Journal of Advanced Computational Intelligence and Intelligent Informatics · doi:10.20965/jaciii.2026.p0543

Training the Right ML Model and Training the ML Model Right

Irvine, J. M., Irizarry, N., Njeunje, F. O. N., & Vilt, S. (2026) · Lecture notes in computer science · doi:10.1007/978-3-032-18474-0_20

Réseaux de neurones convolutionnels profonds pour la détection de petits véhicules en imagerie aérienne

du Terrail, J. O. (2018) · theses.fr (ABES) · doi:10.70675/083d206az0a4ez4480z866ez53e9de431d15

Dust Storm Anomaly Detection on Mars With Event Camera

Chen, H., Xu, C., Gan, J., Leng, J., & Gao, X. (2026) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp55912.2026.11461618

Enhancing Zero-Shot Object Counting via Text-Guided Local Ranking and Number-Evoked Global Attention

Zhang, S., Zhou, Q., & Ke, W. (2025) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51701.2025.01960 · Cited by 3

CountFormer: A Transformer Framework for Learning Visual Repetition and Structure in Class-Agnostic Object Counting

Hossain, M. T., Islam, A., & Ameen, M. R. (2026) · IEEE International Conference on Quantum Photonics, Artificial Intelligence & Networking (QPAIN) · doi:10.1109/qpain69676.2026.11546546

Industrial Object Counting from Traditional Machine Vision to Open-World Foundation Models: A Systematic Review

Wang, W., Zhang, S., He, J., Liu, L., Du, W., & Zhang, L. (2026) · Sensors · doi:10.3390/s26144494

Adversarial Patch and Camouflage Attacks on Aerial Object Detection: A Geometry-Aware Review

Shrestha, S., Mahima, K. T. Y., & Perera, A. G. (2026) · Sensors · doi:10.3390/s26196057

HER-Count: Learning Hyper-Exemplar Representation for Generalized Zero-Shot Object Counting

Li, J., Liu, X., & Xu, R. (2026) · Lecture notes in computer science · doi:10.1007/978-3-032-37225-3_15

PatchMSDNet: an ultra-lightweight patch sampling network for blind ground sampling distance estimation in remote sensing images

Mao, Y., Xiao, L., Gao, Q., & Hao, X. (2026) · International Journal of Remote Sensing · doi:10.1080/01431161.2026.2740241

GSD-Conditioned Generalization of UAV-Trained Oriented Vehicle Detectors Toward Satellite-Relevant Spatial Sampling

Shin, H., & Lee, K. (2026) · Drones · doi:10.3390/drones10100764

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 34

A Systematic Evaluation of Deep Learning Models for Vehicle Classification and Counting Using Numerical Data

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

SCU-Counting: A large-scale benchmark dataset for multi-class object counting

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

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

Deep Neural Networks for Object Enumeration

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