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
High-order Spatial Interactions Enhanced Lightweight Model for Optical Remote Sensing Image-based Small Ship Detection—0
DANet: Enhancing Small Object Detection through an Efficient Deformable Attention Network—0
Context in object detection: a systematic literature review—0
Focus-and-Detect: A Small Object Detection Framework for Aerial Images—0
Context-Aware Block Net for Small Object Detection—0
Fast Fourier Convolution Based Remote Sensor Image Object Detection for Earth Observation—0
Confidence-driven Bounding Box Localization for Small Object Detection—0
Evaluation of YOLO Models with Sliced Inference for Small Object Detection—0
HRDNet: High-resolution Detection Network for Small Objects—0
DASSF: Dynamic-Attention Scale-Sequence Fusion for Aerial Object Detection—0
Progressive Domain Adaptation with Contrastive Learning for Object Detection in the Satellite Imagery—0
Infra-YOLO: Efficient Neural Network Structure with Model Compression for Real-Time Infrared Small Object Detection—0
A Guide to Image and Video based Small Object Detection using Deep Learning : Case Study of Maritime Surveillance—0
MITOS-RCNN: A Novel Approach to Mitotic Figure Detection in Breast Cancer Histopathology Images using Region Based Convolutional Neural Networks—0
Multiple Object Tracking in Recent Times: A Literature Review—0
Ensemble Fusion for Small Object Detection—0
Colonoscopy polyp detection with massive endoscopic images—0
Enhancing Object Detection with Hybrid dataset in Manufacturing Environments: Comparing Federated Learning to Conventional Techniques—0
Enhancing Lightweight Neural Networks for Small Object Detection in IoT Applications—0
Chosen methods of improving small object recognition with weak recognizable features—0
Enhanced Single-shot Detector for Small Object Detection in Remote Sensing Images—0
Dynamic Tiling: A Model-Agnostic, Adaptive, Scalable, and Inference-Data-Centric Approach for Efficient and Accurate Small Object Detection—0
Dynamic Attention and Bi-directional Fusion for Safety Helmet Wearing Detection—0
BFA-YOLO: A balanced multiscale object detection network for building façade attachments detection—0
Analysis of Object Detection Models for Tiny Object in Satellite Imagery: A Dataset-Centric Approach—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