Adaptive NMS: Refining Pedestrian Detection in a Crowd

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Real-Time Detection Transformer for Small-Object Detection Model in Industrial Park Scenes

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SFCW-Net: a spatial-frequency-domain coupling wavelet network for industrial surface defect detection

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Pavement Crack Identification in UAV Images Based on Joint Context Information

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Airport‐YOLO: A Fixed‐Camera Solution for Airport Object Detection

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From Consensus to Causality: Adaptive Reliability Fusion for Object Detection Ensembles

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PBDE: An Effective Method for Filtering False Positive Boxes in Object Detection

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Less false detections, fewer identity switches: methods for the improvement of deep sort

Huang, Z., Tan, K., & Wei, Z. (2021) · Seventh International Symposium on Precision Mechanical Measurements · doi:10.1117/12.2612162 · Cited by 1

Object detection based on multi-modal adaptive fusion using YOLOv3

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Density-NMS: Cell Detection and Classification in Microscopy Images

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改进YOLOv5s算法的地铁场景行人目标检测

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Towards Automatic Animal Classification in Wildlife Environments for Native Species Monitoring in the Amazon

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Pedestrian Detection Using YOLOv5 and Complete-IoU Loss for Autonomous Driving Applications

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Improvement of Soft-YOLOv5 Algorithm for Stray Cats and Dogs Detection

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Multi-Agents with Adapter for Crowd Detection

Cai, Z. (2023) · Smart City Challenges & Outcomes for Urban Transformation (SCOUT) · doi:10.1109/scout58937.2023.00022 · Cited by 1

Real-time Detection Algorithm for Occluded Objects Based on Improved YOLOv5s

Yuan, H., Li, W., & Cheng, Y. H. (2024) · International Seminar on Artificial Intelligence, Networking and Information Technology (AINIT) · doi:10.1109/ainit61980.2024.10581796 · Cited by 1

Rethinking the Non-Maximum Suppression Step in 3D Object Detection from a Bird’s-Eye View

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Multiscale Information Fusion Based on Large Model Inspired Bacterial Detection

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YOLOv5-LC: Enhancing Vehicle Detection for Evening Rushing Hour

Yuan, X., Chen, Q., Li, J., Gong, S., Lin, C., & Hu, X. (2025) · Journal of Transportation Engineering Part A Systems · doi:10.1061/jtepbs.teeng-8652 · Cited by 1

Self-Bootstrapping Pedestrian Detection in Downward-Viewing Fisheye Cameras Using Pseudo-Labeling

Gao, K., Niu, Q., You, H., & Gao, C. (2020) · IEEE International Conference on Multimedia and Expo (ICME) · doi:10.1109/icme46284.2020.9102923 · Cited by 1

Anchor-free pedestrain detection model with semantic context of traffic scenario

Xu, Z., Huang, Y., Chen, S., Nan, Z., & Zheng, N. (2020) · Chinese Automation Congress · doi:10.1109/cac51589.2020.9327716 · Cited by 1

Co-Head Pedestrian Detection in Crowded Scenes

Chen, C., Zhang, M., Tan, H., & Xiao, H. (2021) · IEICE Transactions on Fundamentals of Electronics Communications and Computer Sciences · doi:10.1587/transfun.2020eal2110 · Cited by 1

PBDE: an effective post-processing method based on box density for object detection

Li, Z., Jia, B., He, Y., & Xie, L. (2021) · Applied Intelligence · doi:10.1007/s10489-021-02540-x · Cited by 1

Fast and Fusion: Real-Time Pedestrian Detector Boosted by Body-Head Fusion

Jie, H. J., Gu, X., Liu, X., & Peng, P. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-88004-0_6 · Cited by 1

Gaussian Balanced Sampling for End-to-End Pedestrian Detector

Yang, Y., Li, J., Hou, B., Ren, B., Jiang, X., Cheng, J., & Jiao, L. (2022) · IFIP advances in information and communication technology · doi:10.1007/978-3-031-14903-0_34 · Cited by 1

End-to-End Object Detection with Enhanced Positive Sample Filter

Song, X., Chen, B., Li, P., Wang, B., & Zhang, H. (2023) · Applied Sciences · doi:10.3390/app13031232 · Cited by 1

Learning Mutually in Crowd Scenes for Pedestrian Detection

Wei, R., Wang, Y., & Zhang, J. (2023) · Proceedings - International Conference on Image Processing · doi:10.1109/icip49359.2023.10222019 · Cited by 1

Resisting Crowd Occlusion and Hard Negatives for Pedestrian Detection in the WildPreprint

Wang, Z., Wang, J., & Yang, Y. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2005.07344 · Cited by 3

NRS: Non-Reliability Suppression for False Positives Elimination in YOLOv3

Wang, R., Lee, H., & Kim, J.-S. (2020) · IEEE International Symposium on Circuits and Systems proceedings · doi:10.1109/iscas45731.2020.9181031

Fast and accurate vision-based pedestrian detection

Zhou, C. (2021) · doi:10.32657/10356/148933

A Refined NMS Scheme Based on Center Distance and Corner Distribution

Yang, S., Liu, X., Hu, L., Zhao, H., Zhang, H., Li, L., & Mei, B. (2021) · International Conference on Cloud Computing and Big Data Analytics (ICCCBDA) · doi:10.1109/icccbda51879.2021.9442493

Multi‐dimensional weighted cross‐attention network in crowded scenes

Xie, Y., Zheng, J., Hou, X., Naqvi, I. R., Xi, Y., & Kuang, N. (2021) · IET Image Processing · doi:10.1049/ipr2.12298

A Novel Non-maximum Suppression Algorithm Based on Affinity Propagation Clustering

Xu, G., & Huang, S. (2021) · IEEE International Conference on Industrial Cyber-Physical Systems (ICPS) · doi:10.1109/icps49255.2021.9468162

You Better Look Twice: a new perspective for designing accurate detectors with reduced computations

Dana, A., Shutman, M., Perlitz, Y., Vitek, R., Peleg, T., & Jevnisek, R. J. (2021) · British Machine Vision Conference (BMVC) · doi:10.5244/c.35.124 · Cited by 1

Application of Matrix-NMS in Face Detection and Autonomous Driving

Zhao, X., Yang, P., & Yang, J. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-86137-7_17

Graph Attention Network Based Object Detection and Classification in Crowded Scenario

Xu, G., & Huang, S. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-88207-5_15

Pose-aware monocular localization of occluded pedestrians in 3D scene space

Rahimi, M. M., Khoshelham, K., Stevenson, M., & Winter, S. (2021) · ISPRS Open Journal of Photogrammetry and Remote Sensing · doi:10.1016/j.ophoto.2021.100006

HTS: High-Quality Training Set Selection for Pedestrian Detection

Li, J., Shuang, K., & Zhang, W. (2021) · International Conference on Frontiers of Signal Processing (ICFSP) · doi:10.1109/icfsp53514.2021.9646422

Multi-level Prediction for Overlapped Parcel Segmentation

Zhou, Z., Lyu, S., & Lu, Y. (2022) · Communications in computer and information science · doi:10.1007/978-981-19-2266-4_1

POC: Periodical Orthogonal Center Loss For Open Set Classification

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A Multi-Scenario Crowd Data Synthesis Based On Building Information Modeling

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Learning Consistent Gt-Proposal Assignment for Pedestrian DetectionPreprint

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Visible Part Prediction and Temporal Calibration for Pedestrian Detection

Yang, P., Li, W., Wang, L., Xu, L., & Deng, Q. (2022) · IET Image Processing · doi:10.1049/ipr2.12615

Skew Distribution NMS Algorithm for Text Detection in Natural Scenes

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A Component for Query-based Object Detection in Crowded Scenes

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Inverted Non-Maximum Suppression for More Accurate and Neater Face Detection

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Dense pedestrian detection based on multiple link feature pyramid networks

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An Approach for Neuronal Cell Segmentation Based on Mask-RCNN

Hirapara, P., & Sikander, A. (2023) · Lecture notes in electrical engineering · doi:10.1007/978-981-99-3691-5_36

Agglutination Multivariate Optimal Spanning SOLOv3 Algorithm: Novel Augment-Based Approach for Early-Stage Brain Tumor Image SegmentationPreprint

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A Joint Human and Face Matching Detection Algorithm Based on YOLOv7-tiny

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High-accuracy low-latency non-maximum suppression processor for traffic object detection

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On the Use of Deep Learning Models for Automatic Animal Classification of Native Species in the Amazon

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An Enhanced YOLO Failure Detection Method

Senarathna, D., Ferdous, R., & Tragoudas, S. (2023) · International Conference on Machine Learning and Applications · doi:10.1109/icmla58977.2023.00142

Research on Real-Time Pedestrian Detection Based on Infrared-Visible Image Fusion

Shang, Z., Cheng, B., Xie, X., Fu, T., & Wu, Z. (2023) · International Conference on Sensing, Measurement & Data Analytics in the era of Artificial Intelligence (ICSMD) · doi:10.1109/icsmd60522.2023.10490542

Occluded Pedestrian Detection Model in Dense Scenes Based on Improved Yolov8Preprint

Zhang, Y., Zhao, C., Liu, L., Song, Z., Ma, L., & Chen, Q. (2024) · SSRN Electronic Journal · doi:10.2139/ssrn.4837937

Dual Subnet Label Assignment for End-to-End Fully Convolutional Pedestrian Detection in Crowd Scenes

Wang, J. (2024) · Academic Journal of Computing & Information Science · doi:10.25236/ajcis.2024.070501

Optimizing Image Format P&Id Recognition: Integrating Symbol and Text Recognition with a Single Backbone ArchitecturePreprint

Byun, J., Kang, B., Mun, D., Lee, G., & Kim, H. (2024) · SSRN Electronic Journal · doi:10.2139/ssrn.4841942

Amodal Instance Segmentation of Thin Objects with Large Overlaps by Seed-to-Mask Extending

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Query-Based Multiview Detection for Multiple Visual Sensor Networks

Hsu, H.-M., Yuan, X., Chuang, Y.-Y., Sun, W., & Chang, R.-I. (2024) · Sensors · doi:10.3390/s24154773

Global remote feature modulation end-to-end detection

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Evaluation of Public Participation and Emotion Classification for the Reconstruction of Old Communities Based on Semantic Analysis Algorithm

Gao, M. (2024) · IEEE Access · doi:10.1109/access.2024.3441537

CSP-Lite: Real-Time and Efficient Keypoint-Based Pedestrian Detection

Jia, Y., Pan, H., Wang, J., & Sun, W. (2024) · IEEE Transactions on Emerging Topics in Computational Intelligence · doi:10.1109/tetci.2024.3440193

Attention-Guided Sample-Based Feature Enhancement Network for Crowded Pedestrian Detection Using Vision Sensors

Tang, S., Zhou, Y., Li, J., Liu, C., & Shi, J. (2024) · Sensors · doi:10.3390/s24196350 · Cited by 1

Cascading Refinement Network for Pedestrian Detection

Wang, H. E., Wu, F., Xue, Z., Zhu, X., & Wang, N. (2024) · International Conference on Algorithm, Image Processing and Machine Vision (AIPMV) · doi:10.1109/aipmv62663.2024.10692275

YOLOv10 Improvement Approach Based on ROS Robot Visual Obstacle Avoidance System

Zhong, H., Chen, Z., Shang, Q., & Long, Z. (2024) · International Conference on Cyber Security and Information Engineering (ICCSIE) · doi:10.1145/3689236.3695385

Pedestrian Attribute Distillation Fusion Model

Ding, H., Gong, Y., Han, S., Ding, H., Shang, H., Wang, H., & Liu, J. (2025) · Springer eBooks · doi:10.1007/978-981-96-1108-9_24

Ship Detection via Center Segmentation and Angle Prediction

Lei, Y., Meng, H., Li, L., Ma, J., & Zhang, H. (2025) · Lecture notes in electrical engineering · doi:10.1007/978-981-96-2260-3_2

Pedestrian detection based on vision-language semantics with global adaptive adjustment

Guo, Y., Li, F., Qiu, Y., Xu, P., & Li, K. (2025) · Pattern Recognition Letters · doi:10.1016/j.patrec.2025.03.030

YOLO-Human: A Simple and Effective Anchor-free Network for Dense Pedestrian Detection

Han, W., & He, N. (2024) · International Conference on Image Processing, Computer Vision and Machine Learning (ICICML) · doi:10.1109/icicml63543.2024.10957789

DenseDetect: Exploring End-to-End Human Detection Models for Dense and Occluded Scenes

LI, Z., Li, K., & Qu, S. (2025) · International Conference on Control Engineering and Artificial Intelligence (CCEAI) · doi:10.1145/3722150.3722159

The design and implementation of a student classroom behavior recognition system based on YOLOv8

Ma, S., Yuan, J., Chang, H., Han, J., Chen, Q., & Wei, Z. (2024) · International Conference on Information Science and Education (ICISE-IE) · doi:10.1109/icise-ie64355.2024.11025400

Classification-Based False Alarm Suppression for SAR Target Detection

Yang, N., Huang, L., An, Z., Xu, Y., Hong, X., & Ling, B. W. (2025) · IEEE International Symposium on Circuits and Systems proceedings · doi:10.1109/iscas56072.2025.11043329

GSPNet: A Crowded Pedestrian Detection Network Integrating Global Context, Spatial, and Pose Information

Chang, H., Li, M., Huang, S., & Li, M. (2025) · IEEE Access · doi:10.1109/access.2025.3599213

Fast Bounding Box Hierarchy

Zhu, Z., Jha, S., Kim, J., Gupta, A., & Bau, T. C. (2025) · Proceedings - International Conference on Image Processing · doi:10.1109/icip55913.2025.11084400

FCD-Net: Feature Decorrelation and Confidence-Driven Dynamic Fusion for Robust Pedestrian Recognition in Autonomous Driving

Wang, C., & Xie, H. (2025) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2025.3596166 · Cited by 3

Intelligent Detection of Sports Fields Based on Very High-Resolution Stereo Satellite Imagery

Shang, D., Zhang, Y., Dai, C., & Zhang, L. (2025) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2025.3600107

A Comprehensive Review of Pedestrian Detection on Video and Still Images Using Deep Learning Approaches

Das, D., & Dutta, R. (2025) · Lecture notes in networks and systems · doi:10.1007/978-981-96-6300-2_23

Robust Pedestrian Detection for Autonomous Vehicles Using a Fortified Network Model

Tsai, Y.-Y., & Chang, H.-H. (2025) · International Conference on Man-Machine Systems (ICoMMS) · doi:10.1109/icomms66553.2025.11200432

Detection of pulsed laser in smoke based on time series concatenation and convolutional neural networks

Ding, X., Liu, B., Liang, C., & Niu, L. (2025) · Journal of Optics · doi:10.1007/s12596-025-02964-5

Improving Automatic Check-Out Accuracy with Fine-tuned YOLOv10 and YOLOv12 Models

Skinderowicz, R. (2025) · Procedia Computer Science · doi:10.1016/j.procs.2025.09.577

Optimization of GAM Multi-scene Adaptive Pedestrian Detection Based on YOLOv8n

Lin, X. (2025) · IEEE International Conference on Sensors, Electronics and Computer Engineering (ICSECE) · doi:10.1109/icsece65727.2025.11257035

Integrating Global Detection and Local Keypoints for Occluded Pedestrian Detection

Li, Y., Wang, Y., Huang, Z., & Zhang, D. (2025) · International Conference on Artificial Intelligence, Automation and High Performance Computing (AIAHPC) · doi:10.1109/aiahpc66801.2025.11290304

EFPNet: An Efficient Feature Perception Network for Real-Time Detection of Small UAV Targets

Huang, J., Jin, W., Tao, H., Yunsong, F. E. N. G., Shang, Y., Wang, S., & Liu, A. (2026) · Remote Sensing · doi:10.3390/rs18020340

CEN‐RTDETR: A Co‐Enhancement‐Based Real‐Time Single‐Domain Generalized Object Detection for Road Scenes

Geng, H., Fang, L., Wang, Y. J., Liu, Z., & Fan, Z. (2026) · IET Image Processing · doi:10.1049/ipr2.70294

Variational Box Representation with Normalizing Flows for Pedestrian Detection

He, H. (2026) · Lecture notes in computer science · doi:10.1007/978-981-95-5758-5_28

Automated Instance Segmentation Network for Overlapping Cells in Cleavage-Stage Embryos

Sun, C., Liu, H., Shan, G., Hu, A., Song, H., Chen, W., Chen, L., Wang, S. F., Sun, H., & Sun, Y. (2026) · IEEE Transactions on Automation Science and Engineering · doi:10.1109/tase.2026.3665874

A multi-scale neural architecture enhanced with attention and super-resolution for aerial small object detection

Neri, F., Zhang, Z., & Xue, Y. (2026) · Integrated Computer-Aided Engineering · doi:10.1177/10692509261421247

Multi-scale Graph Convolutional Attention for Leather Fabric Surface Defect Detection

Li, P., Zhao, Y., & Ou, X. (2025) · International Conference on Computer Science and Management Technology (ICCSMT) · doi:10.1145/3795154.3795287

An Occlusion-Resistant Overlapping Pedestrian Detection Algorithm for Crowded Scenes

Ma, X., Li, N., Wu, D., Yidou, L., & Yu, X. H. (2025) · China Automation Congress (CAC) · doi:10.1109/cac67268.2025.11486982

E-SAM: Training-Free Segment Every Entity Model

Zhang, W., Xiao, D., Chen, L., & Wang, L. (2025) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51701.2025.02289

EFGNet: Efficient filter guided network for dense pedestrian detection

Mei, J., Lyu, W., & Lou, X. (2026) · Signal Processing Image Communication · doi:10.1016/j.image.2026.117584

Selective Synergistic Learning for Video Object-Centric Learning

Moon, W., & Heo, J. (2026) · Lecture notes in computer science · doi:10.1007/978-3-032-37383-0_2

Strict NMS: Pedestrian Detection in Crowd Scenes

Zhang, Z., Wang, Y., Jiang, H., & Zeng, X. (2020) · IEEE International Conference on Information Systems and Computer Aided Education (ICISCAE) · doi:10.1109/iciscae51034.2020.9236802 · Cited by 3

OTP-NMS: Toward Optimal Threshold Prediction of NMS for Crowded Pedestrian Detection

Tang, Y., Liu, M., Li, B., Wang, Y., & Ouyang, W. (2023) · IEEE Transactions on Image Processing · doi:10.1109/tip.2023.3273853 · Cited by 33

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

Multi-Attribute NMS: An Enhanced Non-Maximum Suppression Algorithm for Pedestrian Detection in Crowded Scenes

Wang, W., Li, X., Lyu, X., Zeng, T., Chen, J., & Chen, S. (2023) · Applied Sciences · doi:10.3390/app13148073 · Cited by 10

NMS by Representative Region: Towards Crowded Pedestrian Detection by Proposal Pairing

Huang, X., Ge, Z., Jie, Z., & Yoshie, O. (2020) · 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/cvpr42600.2020.01076 · Cited by 205