Small Object Detection

Small Object Detection is a computer vision task that involves detecting and localizing small objects in images or videos. This task is challenging due to the small size and low resolution of the objects, as well as other factors such as occlusion, background clutter, and variations in lighting conditions.

( Image credit: Feature-Fused SSD )

Papers

Recently Added Most Hyped Most Active Needs Verification Most Verified

Showing 131–140 of 152 papers

Title	Date	Tasks	Status	Hype
Review of data analysis in vision inspection of power lines with an in-depth discussion of deep learning technology	Mar 22, 2020	Fault Diagnosisobject-detection	—Unverified	0
Small-Object Detection in Remote Sensing Images with End-to-End Edge-Enhanced GAN and Object Detector Network	Mar 20, 2020	Generative Adversarial NetworkImage Enhancement	CodeCode Available	1
Extended Feature Pyramid Network for Small Object Detection	Mar 16, 2020	Objectobject-detection	CodeCode Available	1
DeepSperm: A robust and real-time bull sperm-cell detection in densely populated semen videos	Mar 3, 2020	Cell DetectionData Augmentation	—Unverified	0
Small Object Detection using Context and Attention	Dec 13, 2019	Objectobject-detection	—Unverified	0
IPG-Net: Image Pyramid Guidance Network for Small Object Detection	Dec 2, 2019	object-detectionObject Detection	—Unverified	0
Better to Follow, Follow to Be Better: Towards Precise Supervision of Feature Super-Resolution for Small Object Detection	Oct 1, 2019	object-detectionObject Detection	—Unverified	0
Small traffic sign detection from large image	Jun 19, 2019	object-detectionObject Detection	—Unverified	0
The Power of Tiling for Small Object Detection	Jun 11, 2019	Objectobject-detection	—Unverified	0
An Energy and GPU-Computation Efficient Backbone Network for Real-Time Object Detection	Apr 22, 2019	GPUobject-detection	CodeCode Available	1

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All datasets SOD4SB Private Test SOD4SB Public Test Bee4Exp Honeybee Detection SODA-D

Benchmark Results

#	Model	Metric	Claimed	Verified	Status
1	Weighted Box Fusion (WBF)	AP50	30.3	—	Unverified
2	GFL + Test Time Augmentation	AP50	23.7	—	Unverified
3	DL method (YOLOv8 + Ensamble)	AP50	22.9	—	Unverified
4	Swin Transformer + Hierarchical design	AP50	22.6	—	Unverified
5	E2 method (Normalized Gaussian Wasserstein Distance + Switch Hard Augmentation + Multi scale train + Weight Moving Average + CenterNet + VarifocalNet)	AP50	22.1	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	Weighted Box Fusion (WBF)	AP50	77.6	—	Unverified
2	DL method (YOLOv8 + Ensamble)	AP50	73.1	—	Unverified
3	GFL + Test Time Augmentation	AP50	73.1	—	Unverified
4	Swin Transformer + Hierarchical design	AP50	70.2	—	Unverified
5	E2 method (Normalized Gaussian Wasserstein Distance + Switch Hard Augmentation + Multi scale train + Weight Moving Average + CenterNet + VarifocalNet)	AP50	69.6	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	BeeDetector	Average F1	0.86	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	CFINet	mAP@0.5:0.95	30.7	—	Unverified