Small Object Detection
15 papers with code • 1 benchmarks • 3 datasets
Small object detection is the task of detecting small objects.
( Image credit: Feature-Fused SSD )
Benchmarks
| Trend | Dataset | Best Model | Paper Title | Paper | Code | Compare |
|---|---|---|---|---|---|---|
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DOTA
iSAID
Bee4Exp Honeybee Detection
Greatest papers with code
An Energy and GPU-Computation Efficient Backbone Network for Real-Time Object Detection
osmr/imgclsmob
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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 #25 on
Instance Segmentation
on COCO test-dev
Rethinking Rotated Object Detection with Gaussian Wasserstein Distance Loss
yangxue0827/RotationDetection
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Boundary discontinuity and its inconsistency to the final detection metric have been the bottleneck for rotating detection regression loss design.
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.
Reducing Label Noise in Anchor-Free Object Detection
nerminsamet/ppdet
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BMVC 2020
In this paper, we propose a new labeling strategy aimed to reduce the label noise in anchor-free detectors.
Ranked #76 on
Object Detection
on COCO test-dev
Augmentation for small object detection
zzl-pointcloud/Data_Augmentation_Zoo_for_Object_Detection
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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
QueryDet: Cascaded Sparse Query for Accelerating High-Resolution Small Object Detection
ChenhongyiYang/QueryDet-PyTorch
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On the popular COCO dataset, the proposed method improves the detection mAP by 1. 0 and mAP-small by 2. 0, and the high-resolution inference speed is improved to 3. 0x on average.
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.
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.
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.