detection
cocoKAIST-Multispectral-Pedestrian-Detection-Datasetanomaly-detection
On-board Anomaly Detection for Marine Environmental Monitoring — PhiSat-2 Database 2025
This folder contains data (images, metadata, and segmentation masks) used to train and test the marine anomaly detection application developed by the IRMA team at IRT Saint Exupéry for the PhiSat-2 OrbitalAI challenge.
Description
This dataset contains images generated from Sentinel-2 satellite data using the ESA simulator, designed to replicate the characteristics of the… See the full description on the dataset page: https://huggingface.co/datasets/sirbastiano94/anomaly-detection.traffic-vehicle-detection
Edge-AI Traffic Vehicle Detection (UA-DETRAC CCTV)
Part of the Edge-AI Traffic & Vehicle Analytics System repository by thundarstrom.
Dataset Summary
Curated and normalized 23,319 CCTV traffic images from fixed intersection surveillance cameras (UA-DETRAC benchmark). Contains 215,109 annotated bounding boxes in standard YOLO format across 4 vehicle classes: car, bus, truck, and van.
Class Mapping
Class 0 (car): 177,403 bboxes (82.5%)
Class 1 (bus):… See the full description on the dataset page: https://huggingface.co/datasets/PRAS4NTH/traffic-vehicle-detection.ETCI-2021-Flood-Detection
ETCI 2021 Flood Detection Dataset
Description
The ETCI 2021 Flood Detection Dataset is a comprehensive flood detection segmentation dataset that focuses on SAR (Synthetic Aperture Radar) images taken by the ESA Sentinel-1 satellite. This dataset provides pairs of VV (Vertical Transmit, Vertical Receive) and VH (Vertical Transmit, Horizontal Receive) polarization images, which have been processed by the Hybrid Pluggable Processing Pipeline (hyp3). Additionally… See the full description on the dataset page: https://huggingface.co/datasets/blanchon/ETCI-2021-Flood-Detection.Obstacle-Detection-Dataset-YOLO
ROD-Dataset: Real-Time Obstacle Detection for Smartphone-Based Assistive Vision
24,326-image, 25-class YOLO dataset for obstacle detection
This dataset is the data product of our Real-Time Obstacle Detection (ROD) project at Amirkabir University of Technology, Tehran. The project addresses two related public-safety problems on the city sidewalk: the limited situational awareness of people living with visual impairments, and the elevated collision and fall risk for pedestrians… See the full description on the dataset page: https://huggingface.co/datasets/Abtinzandi/Obstacle-Detection-Dataset-YOLO.