The early detection of damaged (partially broken) outdoor insulators in primary distribution systems is of paramount importance for continuous electricity supply and public safety. In this dataset, we present different images and videos for computer vision-based research. The dataset comprises images and videos taken from different sources such as a Drone, a DSLR camera, and a mobile phone camera.
Please find the attached file for complete description
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Parking Slot Detection dataset
angle, type, and location of each parking slot
Parking Slot Detection dataset
angle, type, and location of each parking slot
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Recently, self-driving vehicles have been introduced with several automated features including lane-keep assistance, queuing assistance in traffic-jam, parking assistance and crash avoidance. These self-driving vehicles and intelligent visual traffic surveillance systems mainly depend on cameras and sensors fusion systems.
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This archive contains images and labels for the Idly-Dosa-Vada (IDV) dataset, for use with Yolo (and Tensorflow) object detection frameworks.
This archive contains images and labels for the Idly-Dosa-Vada (IDV) dataset, for use with Yolo (and Tensorflow) object detection frameworks.
The dataset contains 1009 images, and corresponding labels.
The dataset was created by using euclidaug, using only 6 images per class.
Folder structure after extracting idv-dataset-files.zip:
out_images - contains all training images
out_labels - contains labels for each image, in Yolo format
For usage, refer to the IEEE-DL-TAP instructions, which are derived from https://github.com/prabindh/yolo-bins/tree/master/capacito
Step 1: Generate full list of image files, for use in the training process. In Windows, this is done using the below command:
dir /s/b *.jpg > trainingfile.txt
Step 2: Using the above file, and the labelled images and labels, start the training process with Yolo using instructions at https://github.com/prabindh/yolo-bins/tree/master/capacito
Step 3: Perform inference using Yolo
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We photographed Giemsa-stained thick blood smear slides from 150 P. falciparum infected patients at Chittagong Medical College Hospital, Bangladesh, using a smartphone camera for the different microscopic field of views. Images are captured with 100x magnification in RGB color space with a 3024×4032 pixel resolution. An expert slide reader manually annotated each image at the Mahidol-Oxford Tropical Medicine Research Unit (MORU), Bangkok, Thailand. We de-identified all images andtheir annotations, and archived them at the National Library of Medicine (IRB#12972).
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