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 101–125 of 152 papers

TitleStatusHype
Better to Follow, Follow to Be Better: Towards Precise Supervision of Feature Super-Resolution for Small Object Detection—0
BFA-YOLO: A balanced multiscale object detection network for building façade attachments detection—0
Chosen methods of improving small object recognition with weak recognizable features—0
Colonoscopy polyp detection with massive endoscopic images—0
Confidence-driven Bounding Box Localization for Small Object Detection—0
Context-Aware Block Net for Small Object Detection—0
Context in object detection: a systematic literature review—0
DANet: Enhancing Small Object Detection through an Efficient Deformable Attention Network—0
DASSF: Dynamic-Attention Scale-Sequence Fusion for Aerial Object Detection—0
Deep-NFA: a Deep a contrario Framework for Small Object Detection—0
DeepSperm: A robust and real-time bull sperm-cell detection in densely populated semen videos—0
Detecting Small Objects in Thermal Images Using Single-Shot Detector—0
DETR++: Taming Your Multi-Scale Detection Transformer—0
Interpretable Dynamic Graph Neural Networks for Small Occluded Object Detection and Tracking—0
Differentiating Objects by Motion: Joint Detection and Tracking of Small Flying Objects—0
Dr2Net: Dynamic Reversible Dual-Residual Networks for Memory-Efficient Finetuning—0
Dynamic Attention and Bi-directional Fusion for Safety Helmet Wearing Detection—0
Dynamic Tiling: A Model-Agnostic, Adaptive, Scalable, and Inference-Data-Centric Approach for Efficient and Accurate Small Object Detection—0
Enhanced Single-shot Detector for Small Object Detection in Remote Sensing Images—0
Enhancing Lightweight Neural Networks for Small Object Detection in IoT Applications—0
Enhancing Object Detection with Hybrid dataset in Manufacturing Environments: Comparing Federated Learning to Conventional Techniques—0
Ensemble Fusion for Small Object Detection—0
Evaluation of YOLO Models with Sliced Inference for Small Object Detection—0
Fast Fourier Convolution Based Remote Sensor Image Object Detection for Earth Observation—0
Focus-and-Detect: A Small Object Detection Framework for Aerial Images—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
2GFL + Test Time AugmentationAP5073.1—Unverified
3DL method (YOLOv8 + Ensamble)AP5073.1—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