Small Object Detection
40 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 )
Datasets
Latest papers
FOOL: Addressing the Downlink Bottleneck in Satellite Computing with Neural Feature Compression
Further, it embeds context and leverages inter-tile dependencies to lower transfer costs with negligible overhead.
HCF-Net: Hierarchical Context Fusion Network for Infrared Small Object Detection
Infrared small object detection is an important computer vision task involving the recognition and localization of tiny objects in infrared images, which usually contain only a few pixels.
YOLO-Ant: A Lightweight Detector via Depthwise Separable Convolutional and Large Kernel Design for Antenna Interference Source Detection
In this article, an antenna dataset is created to address important antenna interference source detection issues and serves as the basis for subsequent research.
HIC-YOLOv5: Improved YOLOv5 For Small Object Detection
Small object detection has been a challenging problem in the field of object detection.
Transformers in Small Object Detection: A Benchmark and Survey of State-of-the-Art
Transformers have rapidly gained popularity in computer vision, especially in the field of object recognition and detection.
Ensemble Fusion for Small Object Detection
Detecting small objects is often impeded by blurriness and low resolution, which poses substantial challenges for accurately detecting and localizing such objects.
Small Object Detection via Coarse-to-fine Proposal Generation and Imitation Learning
The past few years have witnessed the immense success of object detection, while current excellent detectors struggle on tackling size-limited instances.
BandRe: Rethinking Band-Pass Filters for Scale-Wise Object Detection Evaluation
Scale-wise evaluation of object detectors is important for real-world applications.
MVA2023 Small Object Detection Challenge for Spotting Birds: Dataset, Methods, and Results
Small Object Detection (SOD) is an important machine vision topic because (i) a variety of real-world applications require object detection for distant objects and (ii) SOD is a challenging task due to the noisy, blurred, and less-informative image appearances of small objects.
3D Small Object Detection with Dynamic Spatial Pruning
Specifically, we theoretically derive a dynamic spatial pruning (DSP) strategy to prune the redundant spatial representation of 3D scene in a cascade manner according to the distribution of objects.