Small Object Detection Edit Task
Computer Vision • Object Detection
About
Small object detection is the task of detecting small objects.
( Image credit: Feature-Fused SSD )
Benchmarks
| TREND | DATASET | BEST METHOD | PAPER TITLE | PAPER | CODE | COMPARE |
|---|---|---|---|---|---|---|
|
Subtasks
Greatest papers with code
An Energy and GPU-Computation Efficient Backbone Network for Real-Time Object Detection
• osmr/imgclsmob
• 
As DenseNet conserves intermediate features with diverse receptive fields by aggregating them with dense connection, it shows good performance on the object detection task.
Ranked #13 on
Instance Segmentation
on COCO test-dev
REAL-TIME OBJECT DETECTION SEMANTIC SEGMENTATION SMALL OBJECT DETECTION
Rethinking Rotated Object Detection with Gaussian Wasserstein Distance Loss
• yangxue0827/RotationDetection
• 
Boundary discontinuity and its inconsistency to the final detection metric have been the bottleneck for rotating detection regression loss design.
Reducing Label Noise in Anchor-Free Object Detection
BMVC 2020
• nerminsamet/ppdet
• 
In this paper, we propose a new labeling strategy aimed to reduce the label noise in anchor-free detectors.
Ranked #38 on
Object Detection
on COCO test-dev
Small-Object Detection in Remote Sensing Images with End-to-End Edge-Enhanced GAN and Object Detector Network
Inspired by the success of edge enhanced GAN (EEGAN) and ESRGAN, we apply a new edge-enhanced super-resolution GAN (EESRGAN) to improve the image quality of remote sensing images and use different detector networks in an end-to-end manner where detector loss is backpropagated into the EESRGAN to improve the detection performance.
IMAGE ENHANCEMENT REMOTE SENSING IMAGE CLASSIFICATION SATELLITE IMAGE SUPER-RESOLUTION SMALL OBJECT DETECTION SUPER-RESOLUTION
Augmentation for small object detection
• zzl-pointcloud/Data_Augmentation_Zoo_for_Object_Detection
•
We evaluate different pasting augmentation strategies, and ultimately, we achieve 9. 7\% relative improvement on the instance segmentation and 7. 1\% on the object detection of small objects, compared to the current state of the art method on
INSTANCE SEGMENTATION SEMANTIC SEGMENTATION SMALL OBJECT DETECTION
Structure-measure: A New Way to Evaluate Foreground Maps
Our new measure simultaneously evaluates region-aware and object-aware structural similarity between a SM and a GT map.
SALIENT OBJECT DETECTION SEMANTIC SEGMENTATION SMALL OBJECT DETECTION VIDEO OBJECT DETECTION VIDEO SALIENT OBJECT DETECTION
SWIPENET: Object detection in noisy underwater images
Moreover, inspired by the human education process that drives the learning from easy to hard concepts, we here propose the CMA training paradigm that first trains a clean detector which is free from the influence of noisy data.
Underwater object detection using Invert Multi-Class Adaboost with deep learning
In addition, we propose a novel sample-weighted loss function which can model sample weights for SWIPENet, which uses a novel sample re-weighting algorithm, namely Invert Multi-Class Adaboost (IMA), to reduce the influence of noise on the proposed SWIPENet.
Feature-Fused SSD: Fast Detection for Small Objects
We propose a multi-level feature fusion method for introducing contextual information in SSD, in order to improve the accuracy for small objects.
MDSSD: Multi-scale Deconvolutional Single Shot Detector for Small Objects
• siddhanthaldar/PyTorch_Object_Detection
•
The performance of small object detection, however, is still less than satisfactory because of the deficiency of semantic information on shallow feature maps.