It is a dataset that contains six categories of tomato maturity using the criteria established by the USDA.

 

     

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It is a dataset that contains six categories of tomato maturity using the criteria established by the USDA.

 

     

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Wildfires are one of the deadliest and dangerous natural disasters in the world. Wildfires burn millions of forests and they put many lives of humans and animals in danger. Predicting fire behavior can help firefighters to have better fire management and scheduling for future incidents and also it reduces the life risks for the firefighters. Recent advance in aerial images shows that they can be beneficial in wildfire studies.

Instructions: 

The aerial pile burn detection dataset consists of different repositories. The first one is a raw video recorded using the Zenmuse X4S camera. The format of this file is MP4. The duration of the video is 966 seconds with a Frame Per Second (FPS) of 29. The size of this repository is 1.2 GB. The first video was used for the "Fire-vs-NoFire" image classification problem (training/validation dataset). The second one is a raw video recorded using the Zenmuse X4S camera. The duration of the video is 966 seconds with a Frame Per Second (FPS) of 29. The size of this repository is 503 MB. This video shows the behavior of one pile from the start of burning. The resolution of these two videos is 1280x720.

The third video is 89 seconds of heatmap footage of WhiteHot from the thermal camera. The size of this repository is 45 MB. The fourth one is 305 seconds of GreentHot heatmap with a size of 153 MB. The fifth repository is 25 mins of fusion heatmap with a size of 2.83 GB. All these three thermal videos are recorded by the FLIR Vue Pro R thermal camera with an FPS of 30 and a resolution of 640x512. The format of all these videos is MOV.

The sixth video is 17 mins long from the DJI Phantom 3 camera. This footage is used for the purpose of the "Fire-vs-NoFire" image classification problem (test dataset). The FPS is 30, the size is 32 GB, the resolution is 3840x2160, and the format is MOV.

The seventh repository is 39,375 frames that resized to 254x254 for the "Fire-vs-NoFire" image classification problem (Training/Validation dataset). The size of this repository is 1.3 GB and the format is JPEG.

The eighth repository is 8,617 frames that resized to 254x254 for the "Fire-vs-NoFire" image classification problem (Test dataset). The size of this repository is 301 MB and the format is JPEG.

The ninth repository is 2,003 fire frames with a resolution of 3480x2160 for the fire segmentation problem (Train/Val/Test dataset). The size of this repository is 5.3 GB and the format is JPEG.

The last repository is 2,003 ground truth mask frames regarding the fire segmentation problem. The resolution of each mask is 3480x2160. The size of this repository is 23.4 MB.

The preprint article of this dataset is available here:

https://arxiv.org/pdf/2012.14036.pdf

For more information please find the Table at: 

https://github.com/AlirezaShamsoshoara/Fire-Detection-UAV-Aerial-Image-Classification-Segmentation-UnmannedAerialVehicle

To find other projects and articles in our group:

https://www.cefns.nau.edu/~fa334/

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The LGP dataset (LGPSSD) consists of LGP samples collected from the industrial site through the image acquisition device of LGP defect detection system. In our dataset, NG samples are regarded as positive samples, and OK samples are regarded as negative samples.

Instructions: 

The LGP dataset (LGPSSD) consists of LGP samples collected from the industrial site through the image acquisition device of LGP defect detection system. In our dataset, NG samples are regarded as positive samples, and OK samples are regarded as negative samples. Each sample is a grayscale image with a size of 224 * 224 , and has two types of labels: One is the Mask label which is used to supervise the training process of the segmentation subnet, and the other is the classification label (NG corresponds to 1, and OK corresponds to 0), which is employed to supervise the training process of the decision subnet. The dataset totally contains 422 positive samples and 400 negative samples. 

Characteristics: The difference in density between the light guide point distribution of LGP images, the different size, shape and brightness of LGP defects.

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This dataset contains 1216 data, which are scanned by HIS-RING PACT system.

the data sampling rate of our system is 40 MSa/s, a 128-elements 2.5MHz full-view ring-shaped transducer with 30mm radius. 

 continuous updating.....

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Existing datasets  for reflection symmetry detection contain shapes which are single contour shapes, thus they are not really challenging. We also need to consider how well a symmetry detector works on complex/compound shapes where traditional methods based on contour approach can not. On the other hand, there is only one symmetrical axes for every shape of this dataset. Therefore, this fails to evaluate how a symmetry detector works on a shape containing several symmetrical axis and how good the detection is when the number of symmetrical axis is unknown.

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This dataset consists of orthorectified aerial photographs, LiDAR derived digital elevation models and segmentation maps with 10 classes, acquired through the open data program of the German state North Rhine-Westphalia (https://www.opengeodata.nrw.de/produkte/) and refined with OpenStreeMap. Please check the license information (http://www.govdata.de/dl-de/by-2-0).

Instructions: 

Dataset description

The data was mostly acquired over urban areas in North-Rhine Westphalia, Germany. Since the acquisition dates for the aerial photographs and LiDAR do not match exactly, there can be discrepancies in what they show and in which season, e.g., trees change their leaves or lose them in autumn. In our experience, these differences are not drastic but should be kept in mind.

We have included two Python scripts. plot_examples.py creates the example image used on this website. calc_and_plot_stats.py calculates and plots the class statistics. Furthermore, we published the code to create the dataset at https://github.com/gbaier/geonrw, which makes it easy to extend the dataset with other areas in North-Rhine Westphalia. The repository also contains a PyTorch data loader.

This multimodal dataset should be useful for a variety of tasks. Image segmentation using multiple inputs, height estimation from the aerial photographs, or semantic image synthesis.

Organization

Similar to the original source of the data (https://www.opengeodata.nrw.de/produkte/geobasis/lbi/dop/dop_jp2_f10_paketiert/), we organize all samples by the city they were acquired over. Their filenames, e.g., 345_5668_rgb.jp2 consists of the UTM zone 32N coordinates and the datatype (RGB, DEM or seg for land cover).

File formats

All data is geocoded and can be opened using QGIS (https://www.qgis.org/). The aerial photographs are stored as JPEG2000 files, the land cover maps and digital elevation models both as GeoTIFFs. The accompanying scripts show how to read the data into Python.

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Diabetic Retinopathy is the second largest cause of blindness in diabetic patients. Early diagnosis or screening can prevent the visual loss. Nowadays , several computer aided algorithms have been developed to detect the early signs of Diabetic Retinopathy ie., Microaneurysms. The AGAR300 dataset presented here facilitate the researchers for benchmarking MA detection algorithms using digital fundus images. Currently, we have released the first set of database which consists of 28 color fundus images, shows the signs of Microaneurysm.

Instructions: 

The files corresponding to the work reported in paper titled " A novel automated system of discriminating Microaneurysms in fundus images”. The images  are taken from Fundus photography machine with the resolution of 2448x3264. This dataset contains Diabetic Retinopathy images and users of this dataset should cite the following article.

 

D. Jeba Derwin, S. Tamil Selvi, O. Jeba Singh, B. Priestly Shan,”A novel automated system of discriminating Microaneurysms in fundus images”, Biomedical Signal Processing and Control,Vol.58, 2020, pages: 101839,ISSN 1746-8094, https://doi.org/10.1016/j.bspc.2019.101839.

(http://www.sciencedirect.com/science/article/pii/S1746809419304203)

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