Computer Vision

This is the experimental photo dataset of the article "Digitizing Bamboo Scaffolding: Automatic Structural Mapping and Compliance Inspection". 

Four datasets: DroneVehicle, FLIR, CVC-14, and KAIST
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This dataset presents a large-scale collection of VVC (H.266)–compressed multiview dynamic volumetric video content designed for objective and subjective quality assessment in immersive media applications. The dataset is derived from the 8i Voxelized Full Bodies v2 dataset and focuses exclusively on dynamic human performance sequences captured from four viewpoints, enabling realistic evaluation of motion, view dependency, and compression artefacts in volumetric video.

Anthracnose, caused by fungal pathogens of the genus Colletotrichum, is one of the most destructive postharvest diseases affecting bananas, leading to rapid decay, significant economic losses, and reduced fruit quality. Despite its impact, the availability of real-time, disease-specific datasets capturing the progression of anthracnose-induced decay remains limited. This work presents Banana-Anthracnose, a comprehensive real-time dataset designed to document and analyze banana decay resulting from Colletotrichum-driven anthracnose infection.

We introduce a large-scale cross-view benchmark that establishes precise geometric alignment between ground-level street-view imagery and overhead satellite observations for urban 3D perception. The dataset is constructed upon widely used autonomous-driving datasets and augments each street-view frame with a geographically aligned satellite image crop centered at the camera location and orientation. Such cross-view pairing enables consistent correspondence between object-level semantics in the ground view and static scene structures observable from the aerial perspective.

This dataset provides one hour of raw, time-synchronized measurements collected with a shipborne instrumentation platform for multimodal sea-ice mapping. The sensor suite comprises a LiDAR scanner for fine-grained 3D surface geometry, an RGB camera for colorization and visual interpretation, and embedded IMU and GNSS sensors to support motion estimation and georeferencing.

Data consists of 10000+ gameplay frames in An ".npy " format image can be reconstructed of IMG_SIZE = (84, 84); the screen was monitored of SIZE = {'top': 263, 'left': 337, 'width': 734, 'height': 869} & FPS = 15; it is collected using Python programming, and libraries include MSS, OpenCV-Python, Pynput, and Pyautogui.

Game :  Speed Master

This dataset collection is curated to support research in feature selection. It contains several widely-used benchmark datasets, including COIL-20, DrivFace, GISETTE, ISOLET, ORL, TCGA, USPS, and WarpPIE10P. All datasets have been preprocessed and are provided in .mat format for convenient use in MATLAB or Python environments. This collection aims to facilitate the reproducibility of experimental results for feature selection algorithms.

The BMOD (Biological Multi-Object Detection) dataset is a specialized collection of underwater images designed for object detection in dim-light subsea scenarios. It consists of high-resolution images covering various marine organisms. This dataset serves as the experimental basis for the paper "DAF-YOLO11: An Object Detection Algorithm for Dim-Light Subsea Scenarios," providing challenging samples with low visibility, noise interference, and target occlusion.

In this study, we constructed a container defect dataset derived from real-world operational scenarios. The raw data were collected from Yantai Port, comprising 2,347 high-definition RGB images with a resolution of 1920 *1080 pixels. To ensure data diversity and generalization capability, the image acquisition process covered various lighting conditions, shooting angles, and complex background clutters at the port site. During the data annotation phase, the open-source graphical image annotation tool LabelImg was employed to perform precise bounding box annotations on the defect targets.