This dataset is specifically curated to address the most complex real-world challenges in Automatic Number Plate Recognition (ANPR) for Indian vehicles. Standard OCR pipelines frequently fail on Indian license plates due to highly stylized hand-painted fonts, multi-line square formats, physical obstructions (such as flower garlands and ropes), and severe paint degradation.
Automated debris detection on inland freshwater is usually evaluated on coastal or open-water imagery, where the water surface is largely unobstructed. AquaSurf-Malnad-223 targets the opposite regime: 223 geotagged photographs of vegetation-dense freshwater bodies in the Malnad region of Karnataka, India, captured in a single session in April 2025.
The Cardamom Pod Detection and Yield dataset is an open-source image dataset designed to support reproducible research in cardamom pod detection, maturity-aware classification, and feature-based yield estimation. Despite the economic importance of small cardamom (Elettaria cardamomum), no publicly available, standardized image dataset previously existed for this crop. This dataset addresses this gap by providing 265 annotated RGB images derived from frames captured by a fixed-position EZVIZ H9c outdoor camera installed in a cardamom plantation in Wayanad, Kerala, India.
The European Animal Detection Dataset is introduced to support the development and evaluation of Artificial Intelligence (AI)-based methods for wildlife monitoring using camera trap imagery. Existing large-scale datasets, including COCO, ImageNet, MNIST, Pascal VOC, and Google Open Images, were extensively analyzed for training object detection models. However, these datasets were found insufficient for the specific requirements of European wildlife detection due to limited domain relevance, class imbalance, and a lack of species-specific diversity.
Flies are significant vectors of disease, posing serious risks to both human health and livestock productivity. Their presence in environments such as animal bodies, household areas, and waste sites contributes to the spread of harmful pathogens and contamination. This paper presents a multi-source image dataset of flies collected from diverse real-world environments, including images captured on cattle bodies and surrounding areas under varying conditions of lighting, background, and perspective.
The absence of publicly available, large-scale, high-quality datasets for Synthetic Aperture Radar Automatic Target Recognition (SAR ATR) has significantly hindered the application of rapidly advancing deep learning techniques, which hold huge potential to unlock new capabilities in this field. This is primarily because collecting large volumes of diverse target samples from SAR images is prohibitively expensive, largely due to privacy concerns, the characteristics of microwave radar imagery perception, and the need for specialized expertise in data annotation.
MI-OAD: Multi-instance Open-set Aerial Dataset
Fruit ImageNet is a comprehensive, curated collection of high-resolution fruit imagery systematically aggregated from multiple leading search engines, including Google and Bing. Specifically engineered for advanced computer vision and machine learning tasks, this dataset features a robust hierarchical structure organized by variety and source. It provides a diverse range of visual data essential for training deep learning models in object recognition, classification, and quality assessment. Each category undergoes rigorous validation to ensure image integrity and metadata accuracy.
Summary
This dataset is event-based camera data for moving object detection from a static, surveillance-style setup. It targets the use of event-based vision in ITS infrastructure nodes (e.g. cooperative collision avoidance). It contains three object classes (pedestrian, cyclist, car) and three sensor sensitivity levels (RVD: 100 mV, 75 mV, 56 mV), with 2D bounding box labels. It was used to benchmark clustering-based detectors and to study the effect of RVD and accumulation time.
This is the experimental photo dataset of the article "Digitizing Bamboo Scaffolding: Automatic Structural Mapping and Compliance Inspection".
