Small Object Detection
42 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
3D Small Object Detection with Dynamic Spatial Pruning
xuxw98/dspdet3d
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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.
Cascaded Zoom-in Detector for High Resolution Aerial Images
Detecting objects in aerial images is challenging because they are typically composed of crowded small objects distributed non-uniformly over high-resolution images.
Local Contrast and Global Contextual Information Make Infrared Small Object Salient Again
On the other hand, FFC can gain image-level receptive fields and extract global information while preventing small objects from being overwhelmed. Experiments on several public datasets demonstrate that our method significantly outperforms the state-of-the-art ISOS models, and can provide useful guidelines for designing better ISOS deep models.
Towards Scene Understanding for Autonomous Operations on Airport Aprons
The results are quite promising for future applications and provide essential insights regarding the selection of aggregation strategies as well as current potentials and limitations of similar approaches in this research domain.
UIU-Net: U-Net in U-Net for Infrared Small Object Detection
danfenghong/ieee_tip_uiu-net
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RM-DS integrates Residual U-blocks into a deep supervision network to generate deep multi-scale resolution-maintenance features while learning global context information.
Roboflow 100: A Rich, Multi-Domain Object Detection Benchmark
roboflow-ai/roboflow-100-benchmark
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The evaluation of object detection models is usually performed by optimizing a single metric, e. g. mAP, on a fixed set of datasets, e. g. Microsoft COCO and Pascal VOC.
SuperYOLO: Super Resolution Assisted Object Detection in Multimodal Remote Sensing Imagery
Furthermore, we design a simple and flexible SR branch to learn HR feature representations that can discriminate small objects from vast backgrounds with low-resolution (LR) input, thus further improving the detection accuracy.
Occupancy-MAE: Self-supervised Pre-training Large-scale LiDAR Point Clouds with Masked Occupancy Autoencoders
This work proposes a solution to reduce the dependence on labelled 3D training data by leveraging pre-training on large-scale unlabeled outdoor LiDAR point clouds using masked autoencoders (MAE).
Small Object Detection via Pixel Level Balancing With Applications to Blood Cell Detection
hubindijun/faster-rcnn-plb-PyTorch
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Frontiers in Physiology 2022
This method can perform well with blood cell detection in our experiments.
Slicing Aided Hyper Inference and Fine-tuning for Small Object Detection
PaddlePaddle/PaddleDetection
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In this work, an open-source framework called Slicing Aided Hyper Inference (SAHI) is proposed that provides a generic slicing aided inference and fine-tuning pipeline for small object detection.
