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
Automatic detection of aerial survey ground control points based on Yolov5-OBB—0
Confidence-driven Bounding Box Localization for Small Object Detection—0
Deep-NFA: a Deep a contrario Framework for Small Object Detection—0
Local Contrast and Global Contextual Information Make Infrared Small Object Salient AgainCode1
Flying Bird Object Detection Algorithm in Surveillance Video Based on Motion Information—0
ScaleKD: Distilling Scale-Aware Knowledge in Small Object Detector—0
LSTFE-Net:Long Short-Term Feature Enhancement Network for Video Small Object DetectionCode1
An advanced YOLOv3 method for small object detection—0
Towards Scene Understanding for Autonomous Operations on Airport ApronsCode1
UIU-Net: U-Net in U-Net for Infrared Small Object DetectionCode1
Roboflow 100: A Rich, Multi-Domain Object Detection BenchmarkCode2
iSmallNet: Densely Nested Network with Label Decoupling for Infrared Small Target Detection—0
PSA-Det3D: Pillar Set Abstraction for 3D object Detection—0
SuperYOLO: Super Resolution Assisted Object Detection in Multimodal Remote Sensing ImageryCode2
Multiple Object Tracking in Recent Times: A Literature Review—0
Progressive Domain Adaptation with Contrastive Learning for Object Detection in the Satellite Imagery—0
Fast Fourier Convolution Based Remote Sensor Image Object Detection for Earth Observation—0
Chosen methods of improving small object recognition with weak recognizable features—0
Towards Large-Scale Small Object Detection: Survey and Benchmarks—0
A Guide to Image and Video based Small Object Detection using Deep Learning : Case Study of Maritime Surveillance—0
Occupancy-MAE: Self-supervised Pre-training Large-scale LiDAR Point Clouds with Masked Occupancy AutoencodersCode2
Small Object Detection via Pixel Level Balancing With Applications to Blood Cell DetectionCode0
DETR++: Taming Your Multi-Scale Detection Transformer—0
Enhanced Single-shot Detector for Small Object Detection in Remote Sensing Images—0
RangeSeg: Range-Aware Real Time Segmentation of 3D LiDAR Point Clouds—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