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 51–75 of 152 papers

TitleStatusHype
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
Context in object detection: a systematic literature review—0
Small Object Detection: A Comprehensive Survey on Challenges, Techniques and Real-World Applications—0
YOLO-LLTS: Real-Time Low-Light Traffic Sign Detection via Prior-Guided Enhancement and Multi-Branch Feature Interaction—0
Multi-Point Positional Insertion Tuning for Small Object Detection—0
PanSR: An Object-Centric Mask Transformer for Panoptic SegmentationCode0
Analysis of Object Detection Models for Tiny Object in Satellite Imagery: A Dataset-Centric Approach—0
YOLOv5-Based Object Detection for Emergency Response in Aerial Imagery—0
Dynamic Attention and Bi-directional Fusion for Safety Helmet Wearing Detection—0
Interpretable Dynamic Graph Neural Networks for Small Occluded Object Detection and Tracking—0
SL-YOLO: A Stronger and Lighter Drone Target Detection Model—0
Interactive Image-Based Aphid Counting in Yellow Water Traps under Stirring Actions—0
LAM-YOLO: Drones-based Small Object Detection on Lighting-Occlusion Attention Mechanism YOLO—0
Robust infrared small target detection using self-supervised and a contrario paradigms—0
Self-Supervised Learning for Real-World Object Detection: a Survey—0
BFA-YOLO: A balanced multiscale object detection network for building façade attachments detection—0
Small Object Detection for Indoor Assistance to the Blind using YOLO NAS Small and Super Gradients—0
IDD-YOLOv5: A Lightweight Insulator Defect Real-time Detection AlgorithmCode0
Enhancing Object Detection with Hybrid dataset in Manufacturing Environments: Comparing Federated Learning to Conventional Techniques—0
Infra-YOLO: Efficient Neural Network Structure with Model Compression for Real-Time Infrared Small Object Detection—0
XS-VID: An Extremely Small Video Object Detection Dataset—0
DASSF: Dynamic-Attention Scale-Sequence Fusion for Aerial Object Detection—0
Sense Less, Generate More: Pre-training LiDAR Perception with Masked Autoencoders for Ultra-Efficient 3D SensingCode0
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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