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 1–10 of 152 papers

TitleStatusHype
VME: A Satellite Imagery Dataset and Benchmark for Detecting Vehicles in the Middle East and BeyondCode0
Active-O3: Empowering Multimodal Large Language Models with Active Perception via GRPO—0
MAFE R-CNN: Selecting More Samples to Learn Category-aware Features for Small Object Detection—0
Application of YOLOv8 in monocular downward multiple Car Target detection—0
Learning to Borrow Features for Improved Detection of Small Objects in Single-Shot Detectors—0
MASF-YOLO: An Improved YOLOv11 Network for Small Object Detection on Drone View—0
HMPE:HeatMap Embedding for Efficient Transformer-Based Small Object Detection—0
self-prompting analogical reasoning for uav object detectionCode2
Context in object detection: a systematic literature review—0
Small Object Detection: A Comprehensive Survey on Challenges, Techniques and Real-World Applications—0
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All datasetsSOD4SB Private TestSOD4SB Public TestBee4Exp Honeybee DetectionSODA-D

Benchmark Results

#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