SOTAVerified

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

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Showing 26–50 of 152 papers

TitleStatusHype
Transformers in Small Object Detection: A Benchmark and Survey of State-of-the-ArtCode1
3D Small Object Detection with Dynamic Spatial PruningCode1
Reducing Label Noise in Anchor-Free Object DetectionCode1
MWIRSTD: A MWIR Small Target Detection DatasetCode1
YOLO-Z: Improving small object detection in YOLOv5 for autonomous vehiclesCode1
LSTFE-Net:Long Short-Term Feature Enhancement Network for Video Small Object DetectionCode1
PK-YOLO: Pretrained Knowledge Guided YOLO for Brain Tumor Detection in Multiplanar MRI SlicesCode1
A Method for Detection of Small Moving Objects in UAV VideosCode1
HIC-YOLOv5: Improved YOLOv5 For Small Object DetectionCode1
A Comprehensive Approach for UAV Small Object Detection with Simulation-based Transfer Learning and Adaptive FusionCode1
Local-Global Attention: An Adaptive Mechanism for Multi-Scale Feature IntegrationCode1
Multimodal Transformer Using Cross-Channel attention for Object Detection in Remote Sensing ImagesCode1
MVA2023 Small Object Detection Challenge for Spotting Birds: Dataset, Methods, and ResultsCode1
FLAME Diffuser: Wildfire Image Synthesis using Mask Guided DiffusionCode1
Augmentation for small object detectionCode1
FOVEA: Foveated Image Magnification for Autonomous NavigationCode1
Local Contrast and Global Contextual Information Make Infrared Small Object Salient AgainCode1
Anchor Retouching via Model Interaction for Robust Object Detection in Aerial ImagesCode1
QueryDet: Cascaded Sparse Query for Accelerating High-Resolution Small Object DetectionCode1
Small Object Detection via Coarse-to-fine Proposal Generation and Imitation LearningCode1
The Aircraft Context Dataset: Understanding and Optimizing Data Variability in Aerial DomainsCode1
Application of YOLOv8 in monocular downward multiple Car Target detection—0
DASSF: Dynamic-Attention Scale-Sequence Fusion for Aerial Object Detection—0
A novel Multi to Single Module for small object detection—0
DANet: Enhancing Small Object Detection through an Efficient Deformable Attention Network—0
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All datasetsSOD4SB Private TestSOD4SB Public TestBee4Exp Honeybee DetectionSODA-D

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1Weighted Box Fusion (WBF)AP5030.3—Unverified
2GFL + Test Time AugmentationAP5023.7—Unverified
3DL method (YOLOv8 + Ensamble)AP5022.9—Unverified
4Swin Transformer + Hierarchical designAP5022.6—Unverified
5E2 method (Normalized Gaussian Wasserstein Distance + Switch Hard Augmentation + Multi scale train + Weight Moving Average + CenterNet + VarifocalNet)AP5022.1—Unverified
#ModelMetricClaimedVerifiedStatus
1Weighted Box Fusion (WBF)AP5077.6—Unverified
2DL method (YOLOv8 + Ensamble)AP5073.1—Unverified
3GFL + Test Time AugmentationAP5073.1—Unverified
4Swin Transformer + Hierarchical designAP5070.2—Unverified
5E2 method (Normalized Gaussian Wasserstein Distance + Switch Hard Augmentation + Multi scale train + Weight Moving Average + CenterNet + VarifocalNet)AP5069.6—Unverified
#ModelMetricClaimedVerifiedStatus
1BeeDetectorAverage F10.86—Unverified
#ModelMetricClaimedVerifiedStatus
1CFINetmAP@0.5:0.9530.7—Unverified