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 126–150 of 152 papers

TitleStatusHype
High-order Spatial Interactions Enhanced Lightweight Model for Optical Remote Sensing Image-based Small Ship Detection—0
HMPE:HeatMap Embedding for Efficient Transformer-Based Small Object Detection—0
HRDNet: High-resolution Detection Network for Small Objects—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
Inside-Outside Net: Detecting Objects in Context with Skip Pooling and Recurrent Neural Networks—0
Interactive Image-Based Aphid Counting in Yellow Water Traps under Stirring Actions—0
Intrinsic Relationship Reasoning for Small Object Detection—0
IPG-Net: Image Pyramid Guidance Network for Small Object Detection—0
iSmallNet: Densely Nested Network with Label Decoupling for Infrared Small Target Detection—0
Joint-YODNet: A Light-weight Object Detector for UAVs to Achieve Above 100fps—0
Feature Selective Small Object Detection via Knowledge-based Recurrent Attentive Neural Network—0
LAM-YOLO: Drones-based Small Object Detection on Lighting-Occlusion Attention Mechanism YOLO—0
Learning to Borrow Features for Improved Detection of Small Objects in Single-Shot Detectors—0
MAFE R-CNN: Selecting More Samples to Learn Category-aware Features for Small Object Detection—0
Multiple receptive fields and small-object-focusing weakly-supervised segmentation network for fast object detection—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
Multi-Point Positional Insertion Tuning for Small Object Detection—0
MultiResolution Attention Extractor for Small Object Detection—0
OccupancyDETR: Using DETR for Mixed Dense-sparse 3D Occupancy Prediction—0
Perceptual Generative Adversarial Networks for Small Object Detection—0
Prescriptive and Descriptive Approaches to Machine-Learning Transparency—0
PSA-Det3D: Pillar Set Abstraction for 3D object Detection—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
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