Object Detection
3727 papers with code • 92 benchmarks • 262 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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Robust Object Detection
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Small 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 with no code
Leveraging PointNet and PointNet++ for Lyft Point Cloud Classification Challenge
This study investigates the application of PointNet and PointNet++ in the classification of LiDAR-generated point cloud data, a critical component for achieving fully autonomous vehicles.
Efficient Meta-Learning Enabled Lightweight Multiscale Few-Shot Object Detection in Remote Sensing Images
Presently, the task of few-shot object detection (FSOD) in remote sensing images (RSIs) has become a focal point of attention.
Multi-modal Perception Dataset of In-water Objects for Autonomous Surface Vehicles
This paper introduces the first publicly accessible multi-modal perception dataset for autonomous maritime navigation, focusing on in-water obstacles within the aquatic environment to enhance situational awareness for Autonomous Surface Vehicles (ASVs).
FAD-SAR: A Novel Fishing Activity Detection System via Synthetic Aperture Radar Images Based on Deep Learning Method
This paper proposes a deep learning-based system for detecting fishing activities.
RadSimReal: Bridging the Gap Between Synthetic and Real Data in Radar Object Detection With Simulation
Object detection in radar imagery with neural networks shows great potential for improving autonomous driving.
A Hybrid Approach for Document Layout Analysis in Document images
This paper navigates the complexities of understanding various elements within document images, such as text, images, tables, and headings.
BoostRad: Enhancing Object Detection by Boosting Radar Reflections
Subsequently, a second DNN is employed to detect objects within the boosted reflection image.
Inhomogeneous illuminated image enhancement under extremely low visibility condition
Imaging through fog significantly impacts fields such as object detection and recognition.
Cost-Sensitive Uncertainty-Based Failure Recognition for Object Detection
Therefore, a process that mitigates false detections is crucial for both safety and accuracy.
IMWA: Iterative Model Weight Averaging Benefits Class-Imbalanced Learning Tasks
Model Weight Averaging (MWA) is a technique that seeks to enhance model's performance by averaging the weights of multiple trained models.
