Small Object Detection
42 papers with code • 5 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
| Trend | Dataset | Best Model | Paper | Code | Compare |
|---|---|---|---|---|---|
|
|||||
|
|||||
|
|||||
|
Datasets
DOTA
iSAID
RF100
SOD4SB
SODA-A
Aircraft Context Dataset
SODA-D
USC-GRAD-STDdb
Blood Cell Detection Dataset
Latest papers with no code
FLAME Diffuser: Grounded Wildfire Image Synthesis using Mask Guided Diffusion
Thus, our proposed framework can generate a massive dataset of that images are high-quality and ground truth-paired, which well addresses the needs of the annotated datasets in specific tasks.
YOLO-TLA: An Efficient and Lightweight Small Object Detection Model based on YOLOv5
Additionally, we have incorporated a global attention mechanism into the backbone network.
Small Object Detection by DETR via Information Augmentation and Adaptive Feature Fusion
This allows the model to adaptively fuse feature maps from different levels and effectively integrate feature information from different scales.
Deep Learning-Based Object Detection in Maritime Unmanned Aerial Vehicle Imagery: Review and Experimental Comparisons
To further promote the development of maritime UAV-based object detection, this paper provides a comprehensive review of challenges, relative methods, and UAV aerial datasets.
Enhancing Lightweight Neural Networks for Small Object Detection in IoT Applications
Advances in lightweight neural networks have revolutionized computer vision in a broad range of IoT applications, encompassing remote monitoring and process automation.
S$^3$AD: Semi-supervised Small Apple Detection in Orchard Environments
However, crop detection, e. g., apple detection in orchard environments remains challenging due to a lack of large-scale datasets and the small relative size of the crops in the image.
DANet: Enhancing Small Object Detection through an Efficient Deformable Attention Network
Simultaneously, when evaluated on the Pascal VOC dataset, our model showcases its ability to detect objects across a wide spectrum of categories within complex and small scenes.
Joint-YODNet: A Light-weight Object Detector for UAVs to Achieve Above 100fps
Small object detection via UAV (Unmanned Aerial Vehicle) images captured from drones and radar is a complex task with several formidable challenges.
Dynamic Tiling: A Model-Agnostic, Adaptive, Scalable, and Inference-Data-Centric Approach for Efficient and Accurate Small Object Detection
We introduce Dynamic Tiling, a model-agnostic, adaptive, and scalable approach for small object detection, anchored in our inference-data-centric philosophy.
Small Object Detection for Birds with Swin Transformer
Object detection is the task of detecting objects in an image.
