Image Processing
The dataset folder is divided into two parts. The first part is the Train dataset, which contains 900 Kvasir-SEG data sets and 550 CVC-ClinicDB data sets, with a total of 1450 training images. image is the original image and masks are labels. The next is the test dataset, which contains the remaining images of Kvasir-SEG and CVC-ClinicDB as the test set, and all images of CVC-ColonDB, ETIS, and CVC-300 as the test set images.
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The dataset folder is divided into two parts. The first part is the Train dataset, which contains 900 Kvasir-SEG data sets and 550 CVC-ClinicDB data sets, with a total of 1450 training images. image is the original image and masks are labels. The next is the test dataset, which contains the remaining images of Kvasir-SEG and CVC-ClinicDB as the test set, and all images of CVC-ColonDB, ETIS, and CVC-300 as the test set images.
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The NEU-DET dataset is a collection of images featuring surface defects on hot-rolled steel strips. These defects are categorized into six classes: cracks (cr), inclusions (in), patches (pa), pitted surfaces (ps), rolled-in scale (rs), and scratches (sc). The dataset contains 300 grayscale images for each category, totaling 1800 images, with each image sized at 200×200 pixels.
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The Railway Surface Defect Detection (RSDDs) dataset was created to enhance the safety and reliability of railway transportation. This dataset comprises two subsets: Type-I RSDDs and Type-II RSDDs, which were collected from express and common/heavy haul railways, respectively. Type-I RSDDs consists of 67 images, each measuring 160×1000 pixels, while Type-II RSDDs includes 128 images, each measuring 55×1250 pixels. These images were captured under various lighting conditions to simulate real-world railway manufacturing and maintenance environments.
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In this dataset, we present a novel RGB-Thermal paired dataset, RGBT-1K, comprising 1,000 image pairs specifically curated to support research in multi-modality image processing. The dataset captures diverse indoor and outdoor scenes under varying lighting conditions, offering a robust benchmark for applications in image enhancement, object detection, and scene analysis. The image acquisition process involved using the FLIR A70 thermal camera and the Sony Handycam HDR-CX405, with the latter positioned atop the thermal camera for precise alignment.
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IMU-Blur commenced our evaluation by randomly selecting 8350 clear images (aka. backgrounds) from existing image datasets~\cite{zhou2017places,quattoni2009recognizing}. By capturing IMU data during the motion blur induced by the RealSense D455i camera, we synthesized a dataset of 8350 blurred images accompanied by corresponding blur heat maps. Ultimately, this dataset, namely IMU-Blur, contains 6680 triplets for training and 1670 triplets for testing.
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IMU-Blur commenced our evaluation by randomly selecting 8350 clear images (aka. backgrounds) from existing image datasets~\cite{zhou2017places,quattoni2009recognizing}. By capturing IMU data during the motion blur induced by the RealSense D455i camera, we synthesized a dataset of 8350 blurred images accompanied by corresponding blur heat maps. Ultimately, this dataset, namely IMU-Blur, contains 6680 triplets for training and 1670 triplets for testing.
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PSP-MP, a subway platform passenger standing position dataset created using Blender software. The dataset includes 200 test scenarios. The IMG directory stores binocular images of the subway platform layer, with a single image resolution of 1280 * 720Pixel and a JPG format. The GT directory stores information such as the standing position, height, and orientation of passengers on the platform layer in the platform coordinate system.
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Optical remote sensing images, with their high spatial resolution and wide coverage, have emerged as invaluable tools for landslide analysis. Visual interpretation and manual delimitation of landslide areas in optical remote sensing images by human is labor intensive and inefficient. Automatic delimitation of landslide areas empowered by deep learning methods has drawn tremendous attention in recent years. Mask R-CNN and U-Net are the two most popular deep learning frameworks for image segmentation in computer vision.
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This dataset is from "One-Stage Cascade Refinement Networks for Infrared Small Target Detection." It includes 427 infrared images and 480 targets (due to the lack of infrared sequences, SIRST also contains infrared images at a wavelength of 950 nm, in addition to shortwave and midwave infrared images). Approximately 90% of the images contain only one target, while about 10% have multiple targets (which may be overlooked in sparse/significant methods due to global unique assumptions).
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