Adaptive NMS: Refining Pedestrian Detection in a Crowd

Deep Residual Learning for Image Recognition

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Intersection-Over-Union Similarity-Based Nonmaximum Suppression for Human Pose Estimation in Crowded Scenes

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LLA: Loss-aware Label Assignment for Dense Pedestrian DetectionPreprint

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Consistent GT-Proposal Assignment for Challenging Pedestrian Detection

Luo, Y., Zhao, M., Sun, J., Zhai, G., & Zhang, C. (2024) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2024.3401549 · Cited by 6

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Dam Extraction from High-Resolution Satellite Images Combined with Location Based on Deep Transfer Learning and Post-Segmentation with an Improved MBI

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A Plug and Play Fast Intersection Over Union Loss for Boundary Box Regression

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MR-FPN: Multi-Level Residual Feature Pyramid Text Detection Network Based on Self-Attention Environment

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FE-CSP: a fast and efficient pedestrian detector with center and scale prediction

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Contour Information-Guided Multi-Scale Feature Detection Method for Visible-Infrared Pedestrian Detection

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Dynamic Anchor Selection for Improving Object Localization

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Estimating Maximum Likelihood using the Combined Linear and Nonlinear Function in NMS for Object Detection

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SADet: Learning An Efficient and Accurate Pedestrian Detector

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Simple Pair Pose - Pairwise Human Pose Estimation in Dense Urban Traffic Scenes

Braun, M., Flohr, F. B., Krebs, S., Kreße, U., & Gavrila, D. M. (2021) · IEEE Intelligent Vehicles Symposium · doi:10.1109/iv48863.2021.9575435 · Cited by 6

Auth-Persons: A Dataset for Detecting Humans in Crowds from Aerial Views

Symeonidis, C., Mademlis, I., Pitas, I., & Nikolaidis, N. (2022) · Proceedings - International Conference on Image Processing · doi:10.1109/icip46576.2022.9897612 · Cited by 8

NOH-NMS: Improving Pedestrian Detection by Nearby Objects HallucinationPreprint

Zhou, P., Zhou, C., Peng, P., Du, J., Sun, X., Guo, X., & Huang, F. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2007.13376 · Cited by 6

PSRR-MaxpoolNMS++: Fast Non-Maximum Suppression With Discretization and Pooling

Zhang, T., Chen, C., Liu, Y., Geng, X., Aly, M. M. S., & Lin, J. (2024) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2024.3485898 · Cited by 5

CMF-IoU: Multi-Stage Cross-Modal Fusion 3D Object Detection With IoU Joint Prediction

Ning, Z., Liu, Z., Gao, X., Zuo, Y., Yang, J., Fang, Y., & Liu, W. (2025) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2025.3600527 · Cited by 5

Rethinking low-light knowledge for pedestrian detection in nighttime conditions

Chen, Z., Yao, H., Li, W., Long, W., & Zhang, Y. (2025) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2025.111979 · Cited by 5

Toward Accurate and Robust Pedestrian Detection via Variational Inference

He, H., Lin, W., Zhang, Y., He, T., Li, Y., & Li, J. (2024) · International Journal of Computer Vision · doi:10.1007/s11263-024-02216-2 · Cited by 6

DHLA: Dynamic Hybrid Label Assignment for End-to-End Object Detection

Hu, Z., Chen, S., Yang, H., Wang, D., Zhu, S., & Yan, Y. (2024) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2024.3470230 · Cited by 5

CIFE-YOLOv8: A Dense Pedestrian Detection Model Based on Improved YOLOv8

Wang, N., Huang, S., & Liu, X. (2024) · International Conference on Cloud Computing, Big Data Application and Software Engineering (CBASE) · doi:10.1109/cbase64041.2024.10824419 · Cited by 5

Cot-DCN-YOLO: Self-attention-enhancing YOLOv8s for detecting garbage bins in urban street view images

Dai, J., Xu, W., Zhang, H., Wu, Y., Chen, T., & Li, Y. (2025) · The Egyptian Journal of Remote Sensing and Space Science · doi:10.1016/j.ejrs.2025.01.002 · Cited by 7

A Fast Postprocessing Algorithm for the Overlapping Problem in Wafer Map Detection

Li, Y., & Jian-guo, W. (2021) · Journal of Sensors · doi:10.1155/2021/2682286 · Cited by 5

Real-Time Traffic Monitoring and Status Detection with a Multi-vehicle Tracking System

Wang, L., Lam, C., Law, K. L. E., Ng, B. K., Ke, W., & Im, S. (2022) · Lecture notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering · doi:10.1007/978-3-030-97603-3_2 · Cited by 5

Pedestrian Detection Using YOLOv5 For Autonomous Driving Applications

Reddy, E. R. V., & Thale, S. (2021) · IEEE Transportation Electrification Conference (ITEC-India) · doi:10.1109/itec-india53713.2021.9932534 · Cited by 5

A Coulomb Force Inspired Loss Function for High-Performance Pedestrian Detection

Wang, Z., Wang, J., Yang, Y., & Xing, J. (2022) · IEEE Signal Processing Letters · doi:10.1109/lsp.2022.3220147 · Cited by 5

Swin Transformer for Pedestrian and Occluded Pedestrian Detection

Liang, J.-A., & Ding, J. (2024) · IEEE International Symposium on Circuits and Systems proceedings · doi:10.1109/iscas58744.2024.10558302 · Cited by 5

A Novel Computer Vision System for Efficient Flea Beetle Monitoring in Canola Crop

Ullah, M., Hasan, M. S., Bais, A., Wist, T. J., & Sharpe, S. M. (2024) · IEEE Transactions on AgriFood Electronics · doi:10.1109/tafe.2024.3406329 · Cited by 7

A novel visibility semantic feature-aided pedestrian detection scheme for autonomous vehicles

Sha, M., & Boukerche, A. (2021) · Computer Communications · doi:10.1016/j.comcom.2021.06.009 · Cited by 6

Hybrid attention network and center-guided non-maximum suppression for occluded face detection

Jin, M., Li, H., & Xia, Z. (2022) · Multimedia Tools and Applications · doi:10.1007/s11042-022-13999-2 · Cited by 6

VL-NMS: Breaking Proposal Bottlenecks in Two-stage Visual-language Matching

Zhang, C., Ma, W., Xiao, J., Zhang, H., Shao, J., Zhuang, Y., & Chen, L. (2023) · ACM Transactions on Multimedia Computing Communications and Applications · doi:10.1145/3579095 · Cited by 5

Image-Level Automatic Data Augmentation for Pedestrian Detection

Ma, Y., Liu, M., Tang, Y., Wang, X., & Wang, Y. (2023) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2023.3336760 · Cited by 5

Multi-Scale Structure Perception and Global Context-Aware Method for Small-Scale Pedestrian Detection

Gao, H., Huang, S., Li, M., & Li, M. (2024) · IEEE Access · doi:10.1109/access.2024.3406968 · Cited by 6

Visible Feature Guidance for Crowd Pedestrian Detection

Huang, Z., Yue, K., Deng, J., & Zhou, F. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-68238-5_20 · Cited by 6

PS-RCNN: Detecting Secondary Human Instances in a Crowd via Primary Object SuppressionPreprint

Ge, Z., Jie, Z., Huang, X., Xu, R., & Yoshie, O. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2003.07080 · Cited by 5

V2F-Net: Explicit Decomposition of Occluded Pedestrian DetectionPreprint

Shang, M., Xiang, D., Wang, Z., & Zhou, E. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2104.03106 · Cited by 14

EM-YOLO: high-precision electronic component detection via multi-scale attention and dynamic feature fusion

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SF-Former: Feature-Enhanced Network With Transformer for Pedestrian Detection

Zhou, P., Ning, X., Lv, M., Zhang, L., Zhang, B., & Wen, Z. (2025) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2025.3544365 · Cited by 4

Pedestrian detection based on SENet with attention mechanism

Liu, R. (2023) · IEEE Information Technology and Mechatronics Engineering Conference (ITOEC) · doi:10.1109/itoec57671.2023.10291367 · Cited by 4

GigaHumanDet: Exploring Full-Body Detection on Gigapixel-Level Images

Liu, C., Wei, H., Yang, J., Liu, J., Li, W., Guo, Y., & Fang, L. (2024) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v38i9.28873 · Cited by 4

MBB‐YOLO: A comprehensively improved lightweight algorithm for crowded object detection

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InstLane Dataset and Geometry-Aware Network for Instance Segmentation of Lane Line Detection

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Non-Maximum Suppression for Rotated Object Detection During Merging Slices of High-Resolution Images

Ge, L., & Dou, L. (2024) · IEEE Access · doi:10.1109/access.2024.3470815 · Cited by 4

Head-Dominant Enhancement With Local Count for Better Human Detection in Crowds

Han, S., Ding, H., Han, Z., & Li, H. (2024) · IEEE Transactions on Automation Science and Engineering · doi:10.1109/tase.2024.3488856 · Cited by 5

DHQ-DETR: Distributed and High-Quality Object Query for Enhanced Dense Detection in Remote Sensing

Li, C., Zhang, J., Huo, B., & Xue, Y. (2025) · Remote Sensing · doi:10.3390/rs17030514 · Cited by 4

MAOD: An Efficient Anchor-Free Object Detector Based on MobileDet

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Two-stage lesion detection approach based on dimension-decomposition and 3D context

Jiao, J., Pan, H., Chen, C., Jin, T., Yang, D., & Chen, J. (2021) · Tsinghua Science & Technology · doi:10.26599/tst.2021.9010028 · Cited by 4

Medical Image Character Recognition Based on Multi-scale Neural Convolutional Network

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Improving Pedestrian Detection from a Long-tailed Domain Perspective

Ding, M., Zhang, S., & Yang, J. J. (2021) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/3474085.3475573 · Cited by 4

Joint Re-Detection and Re-Identification for Multi-Object Tracking

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DCIFPN: Deformable cross‐scale interaction feature pyramid network for object detection

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CAPNet: Context and Attribute Perception for Pedestrian Detection

Zhu, Y., Huang, H., Yu, H., Chen, A., & Zhao, G. (2023) · Electronics · doi:10.3390/electronics12081781 · Cited by 4

Efficient Feature Extraction for Non-Maximum Suppression in Visual Person Detection

Symeonidis, C., Mademlis, I., Pitas, I., & Nikolaidis, N. (2023) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp49357.2023.10095074 · Cited by 4

FSFNet: Foreground Score-Aware Fusion for 3-D Object Detector Under Unfavorable Conditions

Lin, J., Yin, H., Yan, J., Jian, K., Lu, Y., Wan-cheng, G., Zhang, H., & Rigoll, G. (2023) · IEEE Sensors Journal · doi:10.1109/jsen.2023.3283018 · Cited by 4

Learning Self-Corrective Network via Adaptive Self-Labeling and Dynamic NMS for High-Performance Long-Term Tracking

Zhang, Z., Xue, W., Zhang, K., Liu, B., Zhang, C., Liu, J., & Chen, S. (2023) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2023.3327486 · Cited by 4

Semantic Fusion-based Pedestrian Detection for Supporting Autonomous Vehicles

Sha, M., & Boukerche, A. (2020) · IEEE Symposium on Computers and Communications (ISCC) · doi:10.1109/iscc50000.2020.9219723 · Cited by 4

Cluster-NMS: Improving Crowded Object Detection through Clustering Pattern

Liao, J., & Tian, H. (2025) · Signal Image and Video Processing · doi:10.1007/s11760-025-04374-3 · Cited by 3

Intelligent Multi-Group Marine Predator Algorithm With Deep Learning Assisted Anomaly Detection in Pedestrian Walkways

Sree, S. R., Lydia, E. L., Ahmed, M. A., Radhika, K., Ishak, M. K., Ammar, K., & Mohamed, M. F. P. (2024) · IEEE Access · doi:10.1109/access.2024.3403549 · Cited by 3

Sponge Backdoor Attack: Increasing the Latency of Object Detection Exploiting Non-Maximum Suppression

Xiao, Y., Ma, J., Yi, P., & Chen, X. (2024) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn60899.2024.10650435 · Cited by 4

Analysis of Scale Sensitivity of Ship Detection in an Anchor-Free Deep Learning Framework

Jiang, Y., Huang, L., Zhang, Z., Nie, B., & Zhang, F. (2022) · Electronics · doi:10.3390/electronics12010038 · Cited by 3

Unreliability-Aware Disentangling for Cross-Domain Semi-supervised Pedestrian Detection

Wu, W., Wu, S., & Wong, H. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-26284-5_12 · Cited by 3

Occlusion Target Detection Based on Improved YOLOv5 Model

Lin, Z., Chen, D., & Zeng, Y. (2022) · China Automation Congress (CAC) · doi:10.1109/cac57257.2022.10055351 · Cited by 3

AFC-Net: adjacent feature complementary for crowded pedestrian detection

Wang, J., Zhao, C., Liu, Z.-Q., & Huo, Z. (2023) · Machine Vision and Applications · doi:10.1007/s00138-023-01439-6 · Cited by 3

Deep Learning object detection models: evolution and evaluation

Bouraya, S., & Belangour, A. (2023) · International Conference on Digital Image Processing · doi:10.1145/3604078.3604126 · Cited by 3

Learning to detect extreme objects for remote sensing images

He, Q., Li, M., Huo, L., & Chen, L. (2024) · International Journal of Machine Learning and Cybernetics · doi:10.1007/s13042-023-02059-2 · Cited by 3

Recognition of occluded pedestrians from the driver's perspective for extending sight distance and ensuring driving safety at signal-free intersections

Qie, K., Wang, J., Li, Z., Wang, Z., & Luo, W. (2024) · Digital Transportation and Safety · doi:10.48130/dts-0024-0007 · Cited by 3

CGMA: An improved multi-attribute CIoU-guided enabled pedestrian detection

Joshi, A., & Diwakar, M. (2024) · Multimedia Tools and Applications · doi:10.1007/s11042-024-20335-3 · Cited by 4

LIS-DETR: small target detection transformer for autonomous driving based on learned inverted residual cascaded group

Chen, Y., Jin, Y., Yang, W., Hu, W., Zhang, Z., Wang, C., & Jiao, X. (2025) · Journal of Electronic Imaging · doi:10.1117/1.jei.34.1.013003 · Cited by 3

Towards General Purpose Object Detection: Deep Dense Grid Based Object Detection

Tesema, S. N., & Bourennane, E. (2020) · International Conference on Innovations in Information Technology · doi:10.1109/iit50501.2020.9299036 · Cited by 3

DVRNet: Decoupled Visible Region Network for Pedestrian Detection

Shi, L., Livermore, C., & Kakadiaris, I. A. (2020) · IEEE International Joint Conference on Biometrics (IJCB) · doi:10.1109/ijcb48548.2020.9304883 · Cited by 3

A Vector Field Histogram Based Fuzzy Planner for Robot Navigation in Dense Crowds

Chen, Y., & Lou, Y. (2021) · IEEE International Conference on Real-time Computing and Robotics (RCAR) · doi:10.1109/rcar52367.2021.9517442 · Cited by 3

Human-Cascaded network for Robust Detection of Occluded Pedestrian

Xu, Z., Fu, X., Feng, D., Li, W., & Liu, Y. (2022) · International Conference on Computing, Networks and Internet of Things (CNIOT) · doi:10.1109/cniot55862.2022.00045 · Cited by 3

Detecting Pedestrian With Incomplete Head Feature in Crowded Situation Based on Transformer

Chen, Z., Lin, Y., Xu, J., Lü, K., & Shou, Y. (2025) · IEEE Signal Processing Letters · doi:10.1109/lsp.2024.3525397 · Cited by 2

Object Detection Post Processing Accelerator Based on Co-Design of Hardware and Software

Yang, D., Chen, L., Hao, X., Ni, M., Chen, M., & Zhang, Y. (2025) · Information · doi:10.3390/info16010063 · Cited by 2

OSS-OCL: Occlusion Scenario Simulation and Occluded-edge Concentrated Learning for pedestrian detection

Lu, K., Zhu, C., Liu, M., & Yin, X.-C. (2025) · Pattern Recognition Letters · doi:10.1016/j.patrec.2025.01.026 · Cited by 2

Optimizing image format piping and instrumentation diagram recognition: Integrating symbol and text recognition with a single backbone architecture

Byun, J., Kang, B., Mun, D., Lee, G., & Kim, H. (2025) · Journal of Computational Design and Engineering · doi:10.1093/jcde/qwaf053 · Cited by 3

ARM-NMS: SHAPE BASED NON-MAXIMUM SUPPRESSION FOR INSTANCE SEGMENTATION IN LARGE SCALE IMAGERY

Michel, A., Gross, W., Hinz, S., & Middelmann, W. (2022) · ISPRS annals of the photogrammetry, remote sensing and spatial information sciences · doi:10.5194/isprs-annals-v-2-2022-291-2022 · Cited by 2

Correlated NMS: Establishing Correlations Between Dense Predictions of Remote Sensing Images

Fu, B., Li, W., Sun, Y., Chen, G., Lei Zhang, A., & Wei, W. (2023) · IEEE International Geoscience and Remote Sensing Symposium (IGARSS) · doi:10.1109/igarss52108.2023.10282477 · Cited by 2

Density-aware Object Detection in Aerial Images

Xu, C., Yang, W. T., Yu, H., Datcu, M. P., & Xia, G.-S. (2023) · International Conference on Digital Image Processing · doi:10.1145/3604078.3604120 · Cited by 2

Joint Object Detection and Re-Identification for 3D Obstacle Multi-Camera Systems

Cortés, I., Beltrán, J., de la Escalera, A., & García, F. (2023) · Sensors · doi:10.3390/s23239395 · Cited by 2

Robust Pedestrian Detection in Inclement Weather and Occluded Scenarios: A Review

Prasad, S. R., & Pankaj, D. S. (2023) · International Conference on Smart Computing and Communications (ICSCC) · doi:10.1109/icscc59169.2023.10334973 · Cited by 2

Context feature fusion and enhanced non-maximum suppression for pedestrian detection in crowded scenes

Shao, Y., Hu, J., Hu, L., Zhang, J., & Wang, X. (2024) · Multimedia Tools and Applications · doi:10.1007/s11042-024-18865-x · Cited by 2

Dense Object Detection Based on De-Homogenized Queries

Huang, Y., Ma, C., Zhou, H. F., Wu, H., & Yuan, G. (2024) · Electronics · doi:10.3390/electronics13122312 · Cited by 2

A fused score computation approach to reflect the overlap between the predicted box and the ground truth in pedestrian detection

Chen, Z., Lin, Y., Xu, J., Lü, K., & Huang, Z. (2024) · IET Image Processing · doi:10.1049/ipr2.13251 · Cited by 2

Scene-intra deep mining via shifted instance refinement for weakly supervised person search

Yin, S., Peng, J., Wang, Z., & Wang, H. (2025) · Knowledge-Based Systems · doi:10.1016/j.knosys.2025.113702 · Cited by 2

People Counting Application with Crowded Scenarios: A Case Study with TV Boxes as Edge Devices

Sato, G. M., da Luz, G. P. C. P., Gonzalez, L. F. G., & Borin, J. F. (2025) · Journal of Internet Services and Applications · doi:10.5753/jisa.2025.5156 · Cited by 2

Double parallel branches FCOS for human detection in a crowd

Song, Q., Wang, H., Yang, L., Xin, X., Liu, C., & Hu, M. (2022) · Multimedia Tools and Applications · doi:10.1007/s11042-022-12439-5 · Cited by 2

Applying of Adaptive Threshold Non-maximum Suppression to Pneumonia Detection

Teng, H., Lu, H., Ye, M., Yan, K., Gao, Z. P., & Jin, Q. (2020) · Communications in computer and information science · doi:10.1007/978-981-15-3415-7_43 · Cited by 2

Selective Kernel and Spatial Grouping Attention Network for Occluded Pedestrian Detection

Wang, Y., Li, Y., Yu, H., & Zhang, Q. (2022) · International Conference on Virtual Reality · doi:10.1109/icvr55215.2022.9847953 · Cited by 2

Pedestrian Detection in Crowded Scenes Based on Cascade R-CNN

Yu, G., Xiang, N., & Pan, C. (2022) · International Conference on Computer Technology Applications (ICCTA) · doi:10.1145/3543712.3543720 · Cited by 2

DSF-net: occluded person re-identification based on dual structure features

Fan, Y., Gong, X., & He, Y. (2022) · Neural Computing and Applications · doi:10.1007/s00521-022-07927-6 · Cited by 2

Location First Non-Maximum Suppression for Uncovered Muck Truck Detection

Zhang, Y., Dehua, L. I. U., Chuanpeng, S. U., & Juncheng, L. I. U. (2022) · IEICE Transactions on Fundamentals of Electronics Communications and Computer Sciences · doi:10.1587/transfun.2022eap1100 · Cited by 2

Micro-Expression Spotting Based on a Short-Duration Prior and Multi-Stage Feature Extraction

Xie, Z., & Cheng, S. (2023) · Electronics · doi:10.3390/electronics12020434 · Cited by 2

NSPDet： real-time nearby-aware pedestrian detection algorithm for multi-scene surveillance at night

Gong, A., Li, Z., & Liang, C. (2023) · Journal of Image and Graphics · doi:10.11834/jig.220834 · Cited by 2

Reconciling global and local optimal label assignments for heavily occluded pedestrian detection

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A Hybrid Self-Attention Model for Pedestrians Detection

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