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 76–100 of 152 papers

TitleStatusHype
Small Object Detection for Indoor Assistance to the Blind using YOLO NAS Small and Super Gradients—0
Small Object Detection for Near Real-Time Egocentric Perception in a Manual Assembly Scenario—0
Small Object Detection using Context and Attention—0
Small Object Detection using Deep Learning—0
Small traffic sign detection from large image—0
SOAR: Advancements in Small Body Object Detection for Aerial Imagery Using State Space Models and Programmable Gradients—0
SOD-MTGAN: Small Object Detection via Multi-Task Generative Adversarial Network—0
Spatio-temporal Tubelet Feature Aggregation and Object Linking in Videos—0
MASF-YOLO: An Improved YOLOv11 Network for Small Object Detection on Drone View—0
3D Regression Neural Network for the Quantification of Enlarged Perivascular Spaces in Brain MRI—0
Active-O3: Empowering Multimodal Large Language Models with Active Perception via GRPO—0
A Guide to Image and Video based Small Object Detection using Deep Learning : Case Study of Maritime Surveillance—0
An advanced YOLOv3 method for small object detection—0
Analysis of Object Detection Models for Tiny Object in Satellite Imagery: A Dataset-Centric Approach—0
A novel Multi to Single Module for small object detection—0
Application of YOLOv8 in monocular downward multiple Car Target detection—0
Automatic detection of aerial survey ground control points based on Yolov5-OBB—0
BandRe: Rethinking Band-Pass Filters for Scale-Wise Object Detection Evaluation—0
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
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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