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 141–150 of 152 papers

TitleStatusHype
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
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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