• Corpus ID: 218613728

RISE Video Dataset: Recognizing Industrial Smoke Emissions

@article{Hsu2020RISEVD,
  title={RISE Video Dataset: Recognizing Industrial Smoke Emissions},
  author={Yen-Chia Hsu and Ting-Hao Kenneth Huang and Ting-yao Hu and Paul Dille and Sean Prendi and Ryan Hoffman and Anastasia Tsuhlares and Randy Sargent and Illah Reza Nourbakhsh},
  journal={ArXiv},
  year={2020},
  volume={abs/2005.06111}
}
Industrial smoke emissions pose a significant concern to human health. Prior works have shown that using Computer Vision (CV) techniques to identify smoke as visual evidence can influence the attitude of regulators and empower citizens in pursuing environmental justice. However, existing datasets do not have sufficient quality nor quantity for training robust CV models to support air quality advocacy. We introduce RISE, the first large-scale video dataset for Recognizing Industrial Smoke… 
STCNet: Spatio-Temporal Cross Network for Industrial Smoke Detection
TLDR
A novel Spatio-Temporal Cross Network (STCNet) is proposed to recognize industrial smoke emissions and designs an efficient and concise spatio-temporal dual pyramid architecture to ensure better fusion of multi-scale spatiotemporal information.
ViscoCam: Smartphone-based Drink Viscosity Control Assistant for Dysphagia Patients
TLDR
ViscoCam is presented, the first liquid viscosity classification system for dysphagia patients or carers, which only requires a smartphone, and is easy to operate, widely deployable, and robust for daily use.

References

SHOWING 1-10 OF 66 REFERENCES
SmokeNet: Satellite Smoke Scene Detection Using Convolutional Neural Network with Spatial and Channel-Wise Attention
TLDR
This paper presents a new large-scale satellite imagery smoke detection benchmark based on Moderate Resolution Imaging Spectroradiometer (MODIS) data, namely USTC_SmokeRS, and proposes a new convolution neural network (CNN) model, SmokeNet, which incorporates spatial and channel-wise attention in CNN to enhance feature representation for scene classification.
Adversarial Adaptation From Synthesis to Reality in Fast Detector for Smoke Detection
TLDR
This paper proposes a method based on two state-of-the-art fast detectors, a single-shot multi-box detector, and a multi-scale deep convolutional neural network, for smoke detection using synthetic smoke image samples, and designs an adversarial training strategy to optimize the model of the adapted detectors, to learn a domain-invariant representation for Smoke detection.
Real-time video fire smoke detection by utilizing spatial-temporal ConvNet features
TLDR
A spatial-temporal based convolutional neural network for video smoke detection, and for real-time detection, is proposed, which utilizes a multitask learning strategy to jointly recognize smoke and estimate optical flow, capturing intra-frame appearance features and inter-frame motion features simultaneously.
Smoke Detection on Video Sequences Using 3D Convolutional Neural Networks
TLDR
This paper develops a joint detection framework based on faster RCNN and 3D CNN, which improves the recognition accuracy significantly and is shown to perform very well in smoke location and recognition.
A Deep Normalization and Convolutional Neural Network for Image Smoke Detection
TLDR
A novel deep normalization and convolutional neural network (DNCNN) with 14 layers to implement automatic feature extraction and classification and to reduce overfitting caused by imbalanced and insufficient training samples is proposed.
Industrial Smoke Detection and Visualization
TLDR
A software tool which integrates an algorithm based on change detection and texture segmentation for identifying smoke emissions, an interactive timeline visualization providing indicators for seeking to interesting events, an autonomous fast-forwarding mode for skipping uninteresting timelapse frames, and a collection of animated smoke images generated automatically according to the algorithm are described.
Deep Smoke Segmentation
...
1
2
3
4
5
...