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Methods/Non Maximum Suppression

Non Maximum Suppression

Computer VisionIntroduced 2000389 papers

Description

Non Maximum Suppression is a computer vision method that selects a single entity out of many overlapping entities (for example bounding boxes in object detection). The criteria is usually discarding entities that are below a given probability bound. With remaining entities we repeatedly pick the entity with the highest probability, output that as the prediction, and discard any remaining box where a IoU≥0.5\text{IoU} \geq 0.5IoU≥0.5 with the box output in the previous step.

Image Credit: Martin Kersner

Papers Using This Method

ECORE: Energy-Conscious Optimized Routing for Deep Learning Models at the Edge2025-07-08Optimization of bi-directional gated loop cell based on multi-head attention mechanism for SSD health state classification model2025-06-13Cost-Efficient LLM Training with Lifetime-Aware Tensor Offloading via GPUDirect Storage2025-06-06Accelerating Autoregressive Speech Synthesis Inference With Speech Speculative Decoding2025-05-21Defect Detection in Photolithographic Patterns Using Deep Learning Models Trained on Synthetic Data2025-05-15StableMotion: Repurposing Diffusion-Based Image Priors for Motion Estimation2025-05-10PaniCar: Securing the Perception of Advanced Driving Assistance Systems Against Emergency Vehicle Lighting2025-05-08Learning to Borrow Features for Improved Detection of Small Objects in Single-Shot Detectors2025-04-30Floating Car Observers in Intelligent Transportation Systems: Detection Modeling and Temporal Insights2025-04-29BBoxCut: A Targeted Data Augmentation Technique for Enhancing Wheat Head Detection Under Occlusions2025-03-31ForcePose: A Deep Learning Approach for Force Calculation Based on Action Recognition Using MediaPipe Pose Estimation Combined with Object Detection2025-03-28ODVerse33: Is the New YOLO Version Always Better? A Multi Domain benchmark from YOLO v5 to v112025-02-20Fast-COS: A Fast One-Stage Object Detector Based on Reparameterized Attention Vision Transformer for Autonomous Driving2025-02-11Adaptive Object Detection for Indoor Navigation Assistance: A Performance Evaluation of Real-Time Algorithms2025-01-30Object Detection for Medical Image Analysis: Insights from the RT-DETR Model2025-01-27Variational U-Net with Local Alignment for Joint Tumor Extraction and Registration (VALOR-Net) of Breast MRI Data Acquired at Two Different Field Strengths2025-01-23Diffusion Model is Effectively Its Own Teacher2025-01-01Optimizing SSD Caches for Cloud Block Storage Systems Using Machine Learning Approaches2024-12-29Comprehensive Multi-Modal Prototypes are Simple and Effective Classifiers for Vast-Vocabulary Object Detection2024-12-23Deep Learning and Hybrid Approaches for Dynamic Scene Analysis, Object Detection and Motion Tracking2024-12-05