Small Object Detection
41 papers with code • 4 benchmarks • 11 datasets
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 )
Benchmarks
These leaderboards are used to track progress in Small Object Detection
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Datasets
DOTA
iSAID
RF100
SOD4SB
SODA-A
Aircraft Context Dataset
SODA-D
USC-GRAD-STDdb
Blood Cell Detection Dataset
Latest papers with no code
Rethinking Intersection Over Union for Small Object Detection in Few-Shot Regime
SIoU improves small object detection in the non-few-shot regime, but this setting is unrealistic in the industry as annotated detection datasets are often too expensive to acquire.
TinyDet: Accurate Small Object Detection in Lightweight Generic Detectors
Small object detection requires the detection head to scan a large number of positions on image feature maps, which is extremely hard for computation- and energy-efficient lightweight generic detectors.
High-order Spatial Interactions Enhanced Lightweight Model for Optical Remote Sensing Image-based Small Ship Detection
Accurate and reliable optical remote sensing image-based small-ship detection is crucial for maritime surveillance systems, but existing methods often struggle with balancing detection performance and computational complexity.
Context-Aware Block Net for Small Object Detection
State-of-the-art object detectors usually progressively downsample the input image until it is represented by small feature maps, which loses the spatial information and compromises the representation of small objects.
Video text tracking for dense and small text based on pp-yoloe-r and sort algorithm
Although end-to-end video text spotting methods based on Transformer can model long-range dependencies and simplify the train process, it will lead to large computation cost with the increase of the frame size in the input video.
A novel Multi to Single Module for small object detection
The performance of small object detectors is often compromised by a lack of pixels and less significant features.
Automatic detection of aerial survey ground control points based on Yolov5-OBB
The use of ground control points (GCPs) for georeferencing is the most common strategy in unmanned aerial vehicle (UAV) photogrammetry, but at the same time their collection represents the most time-consuming and expensive part of UAV campaigns.
Confidence-driven Bounding Box Localization for Small Object Detection
C-BBL quantizes continuous labels into grids and formulates two-hot ground truth labels.
Deep-NFA: a Deep $\textit{a contrario}$ Framework for Small Object Detection
The detection of small objects is a challenging task in computer vision.
Flying Bird Object Detection Algorithm in Surveillance Video Based on Motion Information
A Flying Bird Object Detection algorithm Based on Motion Information (FBOD-BMI) is proposed to solve the problem that the features of the object are not obvious in a single frame, and the size of the object is small (low Signal-to-Noise Ratio (SNR)) in surveillance video.
