Small Object Detection

DOTA is a large-scale dataset for object detection in aerial images. It can be used to develop and evaluate object detectors in aerial images. The images are collected from different sensors and platforms. Each image is of the size in the range from 800 × 800 to 20,000 × 20,000 pixels and contains objects exhibiting a wide variety of scales, orientations, and shapes. The instances in DOTA images are annotated by experts in aerial image interpretation by arbitrary (8 d.o.f.) quadrilateral. We will continue to update DOTA, to grow in size and scope to reflect evolving real-world conditions. Now it has three versions: DOTA-v1.0 contains 15 common categories, 2,806 images and 188, 282 instances. The proportions of the training set, validation set, and testing set in DOTA-v1.0 are 1/2, 1/6, and 1/3, respectively. DOTA-v1.5 uses the same images as DOTA-v1.0, but the extremely small instances (less than 10 pixels) are also annotated. Moreover, a new category, ”container crane” is added. It contains 403,318 instances in total. The number of images and dataset splits are the same as DOTA-v1.0. This version was released for the DOAI Challenge 2019 on Object Detection in Aerial Images in conjunction with IEEE CVPR 2019. DOTA-v2.0 collects more Google Earth, GF-2 Satellite, and aerial images. There are 18 common categories, 11,268 images and 1,793,658 instances in DOTA-v2.0. Compared to DOTA-v1.5, it further adds the new categories of ”airport” and ”helipad”. The 11,268 images of DOTA are split into training, validation, test-dev, and test-challenge sets. To avoid the problem of overfitting, the proportion of training and validation set is smaller than the test set. Furthermore, we have two test sets, namely test-dev and test-challenge. Training contains 1,830 images and 268,627 instances. Validation contains 593 images and 81,048 instances. We released the images and ground truths for training and validation sets. Test-dev contains 2,792 images and 353,346 instances. We released the images but not the ground truths. Test-challenge contains 6,053 images and 1,090,637 instances. Source: https://captain-whu.github.io/DOTA/index.html Image Source: https://captain-whu.github.io/DOTA/

iSAID contains 655,451 object instances for 15 categories across 2,806 high-resolution images. The images of iSAID is the same as the DOTA-v1.0 dataset, which are manily collected from the Google Earth, some are taken by satellite JL-1, the others are taken by satellite GF-2 of the China Centre for Resources Satellite Data and Application. Source: iSAID: A Large-scale Dataset for Instance Segmentation in Aerial Images Image Source: iSAID

RICE is a remote sensing image dataset for cloud removal. The proposed dataset consists of two parts: RICE1 contains 500 pairs of images, each pair has images with cloud and cloudless size of 512512; RICE2 contains 450 sets of images, each set contains three 512512 size images, respectively, the reference picture without clouds, the picture of the cloud and the mask of its cloud. Source: https://github.com/BUPTLdy/RICE_DATASET

SODA-A is a large-scale benchmark specialized for small object detection task under aerial scenes, which has 800203 instances with oriented rectangle box annotation across 9 classes. It contains 2510 high-resolution images extracted from Google Earth.

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. Due to image retrieval and annotation costs, these datasets consist largely of images found on the web and do not represent many real-life domains that are being modelled in practice, e.g. satellite, microscopic and gaming, making it difficult to assert the degree of generalization learned by the model. We introduce the Roboflow-100 (RF100) consisting of 100 datasets, 7 imagery domains, 224,714 images, and 805 class labels with over 11,170 labelling hours. We derived RF100 from over 90,000 public datasets, 60 million public images that are actively being assembled and labelled by computer vision practitioners in the open on the web application Roboflow Universe. By releasing RF100, we aim to provide a semantically diverse, multi-domain benchmark of datasets to help researchers test their model's generalizability with real-life data. RF100 download and benchmark replication are available on GitHub.

SODA-D is a large-scale dataset tailored for small object detection in driving scenario, which is built on top of MVD dataset and owned data, where the former is a dataset dedicated to pixel-level understanding of street scenes, and the latter is mainly captured by onboard cameras and mobile phones. With 24704 well-chosen and high-quality images of driving scenarios, SODA-D comprises 277596 instances of 9 categories with horizontal bounding boxes.

The Small Object Detection for Spotting Birds (SOD4SB) dataset is a dataset consisting of 39,070 images including 137,121 bird instances. The SOD4SD dataset contains a wide variety of small bird types and a variety of scenes.

The Aircraft Context Dataset, a composition of two inter-compatible large-scale and versatile image datasets focusing on manned aircraft and UAVs, is intended for training and evaluating classification, detection and segmentation models in aerial domains. Additionally, a set of relevant meta-parameters can be used to quantify dataset variability as well as the impact of environmental conditions on model performance.

VD4UAV is an altitude-sensitive benchmark dataset designed to evade vehicle detection in Unmanned Aerial Vehicle (UAV) imagery. This dataset is specifically curated to facilitate the study of adversarial patch-based vehicle detection attacks in UAV images. The EVD4UAV dataset comprises a diverse set of images captured at various altitudes with fine-grained annotations, making it a robust platform for evaluating the performance of object detectors under adversarial conditions. Notably, the dataset includes around 3,000 images depicting winter scenarios where vehicles may be partially or fully covered by snow, providing a unique challenge for vehicle detection algorithms.

The Apron Dataset focuses on training and evaluating classification and detection models for airport-apron logistics. In addition to bounding boxes and object categories the dataset is enriched with meta parameters to quantify the models’ robustness against environmental influences.

A dataset for flying honeybee detection introduced in "A Method for Detection of Small Moving Objects in UAV Videos". This dataset consists of three videos with flying honeybees in a natural environment.

Overview This is a dataset of blood cells photos. There are 364 images across three classes: WBC (white blood cells), RBC (red blood cells), and Platelets. There are 4888 labels across 3 classes (and 0 null examples). Fork this dataset (upper right hand corner) to receive the raw images, or (to save space) grab the 500x500 export. ## Use Cases This is a small scale object detection dataset, commonly used to assess model performance. It's a first example of medical imaging capabilities. This dataset is mainly preprocessed for YOLOV5 Application. ##Using this Dataset I'm releasing the data as public domain. Feel free to use it for any purpose. This dataset is already splitted into train,testing and validation datasets(70% for training, 20% testing and 10% for validation). The train,testing and validation folders are further classified as IMAGES AND LABELS. images Folder : It contains images of blood cells. labels Folder : It conatins labelling of blood cells across three classes: WBC (white blood cells), RBC (red blood cells), and Platelets. Except for GitHub, this dataset is also published on Kaggle.

This work contributes a large, complex, and realistic high-quality safety clothing and helmet detection (SFCHD) dataset. The dataset comprises 12,373 images, covering 7 categories, with a total of 50,558 labeled instances. All images are captured from factory surveillance cameras, encompassing 40 different scenes across two chemical plants. It is worth noting that our SFCHD dataset not only provides a rich set of training samples but also serves as a benchmark for the evaluation of various detection tasks, such as small object detection, and high-low light object detection.

USC-GRAD-STDdb comprises 115 video segments containing more than 25,000 annotated frames of HD 720p resolution (≈1280x720) with small objects of interest from 16 (≈4x4) to 256 (≈16x16) as pixel area. The length of the videos changes from 150 up to 500 frames. The size of every object is determined through the bounding box, so that a good annotation is of utmost importance for reliable performance metrics. As it may seem obvious, the smaller the object, the harder the annotation. The annotation has been carried out with the ViTBAT tool, adjusting the boxes as much as possible to the objects of interest in each video frame. In total, more than 56,000 ground truth labels have been generated.