Object Detection
3697 papers with code • 84 benchmarks • 256 datasets
Object Detection is a computer vision task in which the goal is to detect and locate objects of interest in an image or video. The task involves identifying the position and boundaries of objects in an image, and classifying the objects into different categories. It forms a crucial part of vision recognition, alongside image classification and retrieval.
The state-of-the-art methods can be categorized into two main types: one-stage methods and two stage-methods:
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One-stage methods prioritize inference speed, and example models include YOLO, SSD and RetinaNet.
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Two-stage methods prioritize detection accuracy, and example models include Faster R-CNN, Mask R-CNN and Cascade R-CNN.
The most popular benchmark is the MSCOCO dataset. Models are typically evaluated according to a Mean Average Precision metric.
( Image credit: Detectron )
Benchmarks
These leaderboards are used to track progress in Object Detection
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Libraries
Use these libraries to find Object Detection models and implementationsDatasets
MS COCO
KITTI
nuScenes
Visual Genome
LVIS
SUN RGB-D
Waymo Open Dataset
BDD100K
MVTecAD
Manga109
Subtasks
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3D Object Detection
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Real-Time Object Detection
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RGB Salient Object Detection
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Few-Shot Object Detection
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Few-Shot Object Detection
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Video Object Detection
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RGB-D Salient Object Detection
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Open Vocabulary Object Detection
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Object Detection In Aerial Images
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Weakly Supervised Object Detection
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Small Object Detection
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Robust Object Detection
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Medical Object Detection
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Zero-Shot Object Detection
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Open World Object Detection
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Co-Salient Object Detection
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Dense Object Detection
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Object Proposal Generation
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Video Salient Object Detection
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Camouflaged Object Segmentation
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License Plate Detection
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Head Detection
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Multiview Detection
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3D Object Detection From Monocular Images
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One-Shot Object Detection
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Moving Object Detection
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Surgical tool detection
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Described Object Detection
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Body Detection
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Pupil Detection
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Object Detection In Indoor Scenes
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Class-agnostic Object Detection
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Semantic Part Detection
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Object Skeleton Detection
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Fish Detection
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Multiple Affordance Detection
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Weakly Supervised 3D Detection
Latest papers
Multi-resolution Rescored ByteTrack for Video Object Detection on Ultra-low-power Embedded Systems
bomps4/multi_resolution_rescored_bytetrack
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This paper introduces Multi-Resolution Rescored Byte-Track (MR2-ByteTrack), a novel video object detection framework for ultra-low-power embedded processors.
Learning Feature Inversion for Multi-class Anomaly Detection under General-purpose COCO-AD Benchmark
zhangzjn/ader
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Moreover, current metrics such as AU-ROC have nearly reached saturation on simple datasets, which prevents a comprehensive evaluation of different methods.
Low-Light Image Enhancement Framework for Improved Object Detection in Fisheye Lens Datasets
daitranskku/aic2024-track4-team15
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This study addresses the evolving challenges in urban traffic monitoring detection systems based on fisheye lens cameras by proposing a framework that improves the efficacy and accuracy of these systems.
Training-free Boost for Open-Vocabulary Object Detection with Confidence Aggregation
Specifically, in the region-proposal stage, proposals that contain novel instances showcase lower objectness scores, since they are treated as background proposals during the training phase.
SFSORT: Scene Features-based Simple Online Real-Time Tracker
This paper introduces SFSORT, the world's fastest multi-object tracking system based on experiments conducted on MOT Challenge datasets.
ConsistencyDet: A Robust Object Detector with a Denoising Paradigm of Consistency Model
In the present study, we introduce a novel framework designed to articulate object detection as a denoising diffusion process, which operates on the perturbed bounding boxes of annotated entities.
Scaling Multi-Camera 3D Object Detection through Weak-to-Strong Eliciting
Finally, for MC3D-Det joint training, the elaborate dataset merge strategy is designed to solve the problem of inconsistent camera numbers and camera parameters.
Retrieval-Augmented Open-Vocabulary Object Detection
Specifically, RALF consists of two modules: Retrieval Augmented Losses (RAL) and Retrieval-Augmented visual Features (RAF).
Better Monocular 3D Detectors with LiDAR from the Past
Accurate 3D object detection is crucial to autonomous driving.
Detecting Every Object from Events
Object detection is critical in autonomous driving, and it is more practical yet challenging to localize objects of unknown categories: an endeavour known as Class-Agnostic Object Detection (CAOD).
