Image Fusion
<p>ImageNet is a large-scale visual database widely used in the field of computer vision, especially for object recognition tasks. It contains millions of labeled images, organized into multiple categories, and is used for training and evaluating image classification models. ImageNet datasets are widely used for training deep learning models, particularly Convolutional Neural Networks (CNNs). ILSVRC2012 (ImageNet Large Scale Visual Recognition Challenge 2012) is a part of ImageNet and is a competition for image classification and object detection.
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The IARPA Space-Based Machine Automated Recognition Technique (SMART) program was one of the first large-scale research program to advance the state of the art for automatically detecting, characterizing, and monitoring large-scale anthropogenic activity in global scale, multi-source, heterogeneous satellite imagery. The program leveraged and advanced the latest techniques in artificial intelligence (AI), computer vision (CV), and machine learning (ML) applied to geospatial applications.
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This dataset is a large-scale video benchmark constructed for RGB-Thermal (RGB-T) object tracking tasks, featuring the following key characteristics:
1. **Scale & Diversity**
- Contains 234,000 total frames, with sequences up to 8,000 frames
- Covers diverse scenarios and complex environmental conditions
- Currently the largest publicly available RGB-T dataset in the field
2. **Precise Multimodal Alignment**
- Strict spatiotemporal synchronization between RGB and thermal sequences
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This dataset was produced as part of the NANCY project (https://nancy-project.eu/), with the aim of using it in the fields of communication and
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FLAME2-DT (Forest Fire Detection Dataset with Dual-modality Labels) is a comprehensive multi-modal dataset specifically designed for UAV-based forest fire detection research. The dataset consists of 1,280 paired RGB-thermal infrared images captured by a Mavic 2 Enterprise Advanced UAV system, with high-resolution (640×512) and precise pixel-level annotations for both fire and smoke regions. This dataset addresses critical challenges in forest fire detection by providing paired multi-modal data that captures the complementary characteristics of visible light and thermal imaging.
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Repeated Route Naturalistic Driving Dataset (R2ND2) is a dual-perspective dataset for driver behavior analysis constituent of vehicular data collected using task-specific CAN decoding sensors using OBD port and external sensors, and (b) gaze-measurements collected using industry-standard multi-camera gaze calibration and collection system. Our experiment is designed to consider the variability associated with driving experience that depends on the time of day and provides valuable insights into the correlation of these additional metrics on driver behavior.
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Hyperspectral imaging (HSI) has become a pivotal tool for environmental monitoring, particularly in identifying and analyzing hydrocarbon spills. This study presents an Internet of Things (IoT)-based framework for the collection, management, and analysis of hyperspectral data, employing a controlled experimental setup to simulate hydrocarbon contamination. Using a state-of-the-art hyperspectral camera, a dataset of 116 images was generated, encompassing temporal and spectral variations of gasoline, thinner, and motor oil spills.
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The increasing number of wildfires damages nature and human life, making the early detection of wildfires in complex outdoor environments critical. With the advancement of drones and remote sensing technology, infrared cameras have become essential for wildfire detection. However, as the demand for higher accuracy in detection algorithms grows, the detection model's size and computational costs increase, making it challenging to deploy high-precision detection algorithms on edge computing devices onboard drones for real-time fire detection.
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The file contains merely a small portion of our research results. The particular picture presented showcases the outcomes that were achieved through our fusion method when applied to the TNO test set. The "ir", which is the abbreviation for infrared light image, is characterized by being rich in thermal radiation information. However, it unfortunately has a rather low spatial resolution. On the other hand, the "vis", representing the visible light image, is abundant in scene information. But it has a drawback in that the human targets within it are not so distinctly visible.
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The IARPA WRIVA program aims to develop software systems that can create photorealistic, navigable 3D site models using a highly limited corpus of imagery, to include ground level imagery, surveillance height imagery, airborne altitude imagery, and satellite imagery. Additionally, where imagery lacks metadata indicating geolocation, information about camera parameters, or is corrupted by artifacts, WRIVA seeks to detect and correct these factors to incorporate the imagery in site-modelling and other downstream image processing and analysis algorithms.
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