5,585 research outputs found
UbiEar: Bringing location-independent sound awareness to the hard-of-hearing people with smartphones
Non-speech sound-awareness is important to improve the quality of life for the deaf and hard-of-hearing (DHH) people. DHH people, especially the young, are not always satisfied with their hearing aids. According to the interviews with 60 young hard-of-hearing students, a ubiquitous sound-awareness tool for emergency and social events that works in diverse environments is desired. In this paper, we design UbiEar, a smartphone-based acoustic event sensing and notification system. Core techniques in UbiEar are a light-weight deep convolution neural network to enable location-independent acoustic event recognition on commodity smartphons, and a set of mechanisms for prompt and energy-efficient acoustic sensing. We conducted both controlled experiments and user studies with 86 DHH students and showed that UbiEar can assist the young DHH students in awareness of important acoustic events in their daily life.</jats:p
Semi-supervised wildfire smoke detection based on smoke-aware consistency
The semi-transparency property of smoke integrates it highly with the background contextual information in the image, which results in great visual differences in different areas. In addition, the limited annotation of smoke images from real forest scenarios brings more challenges for model training. In this paper, we design a semi-supervised learning strategy, named smokeaware consistency (SAC), to maintain pixel and context perceptual consistency in different backgrounds. Furthermore, we propose a smoke detection strategy with triple classification assistance for smoke and smoke-like object discrimination. Finally, we simplified the LFNet fire-smoke detection network to LFNet-v2, due to the proposed SAC and triple classification assistance that can perform the functions of some specific module. The extensive experiments validate that the proposed method significantly outperforms state-of-the-art object detection algorithms on wildfire smoke datasets and achieves satisfactory performance under challenging weather conditions.Peer ReviewedPostprint (published version
A review of machine learning applications in wildfire science and management
Artificial intelligence has been applied in wildfire science and management
since the 1990s, with early applications including neural networks and expert
systems. Since then the field has rapidly progressed congruently with the wide
adoption of machine learning (ML) in the environmental sciences. Here, we
present a scoping review of ML in wildfire science and management. Our
objective is to improve awareness of ML among wildfire scientists and managers,
as well as illustrate the challenging range of problems in wildfire science
available to data scientists. We first present an overview of popular ML
approaches used in wildfire science to date, and then review their use in
wildfire science within six problem domains: 1) fuels characterization, fire
detection, and mapping; 2) fire weather and climate change; 3) fire occurrence,
susceptibility, and risk; 4) fire behavior prediction; 5) fire effects; and 6)
fire management. We also discuss the advantages and limitations of various ML
approaches and identify opportunities for future advances in wildfire science
and management within a data science context. We identified 298 relevant
publications, where the most frequently used ML methods included random
forests, MaxEnt, artificial neural networks, decision trees, support vector
machines, and genetic algorithms. There exists opportunities to apply more
current ML methods (e.g., deep learning and agent based learning) in wildfire
science. However, despite the ability of ML models to learn on their own,
expertise in wildfire science is necessary to ensure realistic modelling of
fire processes across multiple scales, while the complexity of some ML methods
requires sophisticated knowledge for their application. Finally, we stress that
the wildfire research and management community plays an active role in
providing relevant, high quality data for use by practitioners of ML methods.Comment: 83 pages, 4 figures, 3 table
Wildfire Smoke Detection with Cross Contrast Patch Embedding
The Transformer-based deep networks have increasingly shown significant
advantages over CNNs. Some existing work has applied it in the field of
wildfire recognition or detection. However, we observed that the vanilla
Transformer is not friendly for extracting smoke features. Because low-level
information such as color, transparency and texture is very important for smoke
recognition, and transformer pays more attention to the semantic relevance
between middle- or high-level features, and is not sensitive to the subtle
changes of low-level features along the space. To solve this problem, we
propose the Cross Contrast Patch Embedding(CCPE) module based on the Swin
Transformer, which uses the multi-scales spatial frequency contrast information
in both vertical and horizontal directions to improve the discrimination of the
network on the underlying details. The fuzzy boundary of smoke makes the
positive and negative label assignment for instances in a dilemma, which is
another challenge for wildfires detection. To solve this problem, a Separable
Negative Sampling Mechanism(SNSM) is proposed. By using two different negative
instance sampling strategies on positive images and negative images
respectively, the problem of supervision signal confusion caused by label
diversity in the process of network training is alleviated. This paper also
releases the RealFire Test, the largest real wildfire test set so far, to
evaluate the proposed method and promote future research. It contains 50,535
images from 3,649 video clips. The proposed method has been extensively tested
and evaluated on RealFire Test dataset, and has a significant performance
improvement compared with the baseline detection models
Fully Convolutional Variational Autoencoder For Feature Extraction Of Fire Detection System
This paper proposes a fully convolutional variational autoencoder (VAE) for features extraction from a large-scale dataset of fire images. The dataset will be used to train the deep learning algorithm to detect fire and smoke. The features extraction is used to tackle the curse of dimensionality, which is the common issue in training deep learning with huge datasets. Features extraction aims to reduce the dimension of the dataset significantly without losing too much essential information. Variational autoencoders (VAEs) are powerfull generative model, which can be used for dimension reduction. VAEs work better than any other methods available for this purpose because they can explore variations on the data in a specific direction
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