Object Detection
3820 papers with code • 93 benchmarks • 267 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 )
Libraries
Use these libraries to find Object Detection models and implementationsDatasets
Subtasks
- 3D Object Detection
- Real-Time Object Detection
- RGB Salient Object Detection
- Few-Shot Object Detection
- Few-Shot Object Detection
- Video Object Detection
- Open Vocabulary Object Detection
- RGB-D Salient Object Detection
- Object Detection In Aerial Images
- Weakly Supervised Object Detection
- Small Object Detection
- Robust Object Detection
- Zero-Shot Object Detection
- Medical Object Detection
- Open World Object Detection
- Object Proposal Generation
- Co-Salient Object Detection
- Dense Object Detection
- Video Salient Object Detection
- Camouflaged Object Segmentation
- License Plate Detection
- Head Detection
- Multiview Detection
- 3D Object Detection From Monocular Images
- One-Shot Object Detection
- Surgical tool detection
- Moving Object Detection
- Described Object Detection
- Body Detection
- Pupil Detection
- Object Detection In Indoor Scenes
- Class-agnostic Object Detection
- Semantic Part Detection
- Object Skeleton Detection
- Fish Detection
- Multiple Affordance Detection
- Weakly Supervised 3D Detection
Most implemented papers
You Only Look Once: Unified, Real-Time Object Detection
A single neural network predicts bounding boxes and class probabilities directly from full images in one evaluation.
MMDetection: Open MMLab Detection Toolbox and Benchmark
In this paper, we introduce the various features of this toolbox.
CSPNet: A New Backbone that can Enhance Learning Capability of CNN
Neural networks have enabled state-of-the-art approaches to achieve incredible results on computer vision tasks such as object detection.
FCOS: Fully Convolutional One-Stage Object Detection
By eliminating the predefined set of anchor boxes, FCOS completely avoids the complicated computation related to anchor boxes such as calculating overlapping during training.
Feature Pyramid Networks for Object Detection
Feature pyramids are a basic component in recognition systems for detecting objects at different scales.
Going Deeper with Convolutions
We propose a deep convolutional neural network architecture codenamed "Inception", which was responsible for setting the new state of the art for classification and detection in the ImageNet Large-Scale Visual Recognition Challenge 2014 (ILSVRC 2014).
Objects as Points
We model an object as a single point --- the center point of its bounding box.
Swin Transformer: Hierarchical Vision Transformer using Shifted Windows
This paper presents a new vision Transformer, called Swin Transformer, that capably serves as a general-purpose backbone for computer vision.
Frustum PointNets for 3D Object Detection from RGB-D Data
In this work, we study 3D object detection from RGB-D data in both indoor and outdoor scenes.
Searching for MobileNetV3
We achieve new state of the art results for mobile classification, detection and segmentation.