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 to pursue environmental justice. However, existing datasets are not of sufficient quality nor quantity to train the robust CV models needed to support air quality advocacy. We introduce RISE, the first large-scale video dataset for Recognizing Industrial Smoke Emissions. We adopted a citizen science approach to collaborate with local community members to annotate whether a video clip has smoke emissions. Our dataset contains 12,567 clips from 19 distinct views from cameras that monitored three industrial facilities. These daytime clips span 30 days over two years, including all four seasons. We ran experiments using deep neural networks to establish a strong performance baseline and reveal smoke recognition challenges. Our survey study discussed community feedback, and our data analysis displayed opportunities for integrating citizen scientists and crowd workers into the application of Artificial Intelligence for Social Impact.
Industrial smoke emissions present a serious threat to natural ecosystems and human health. Prior works have shown that using computer vision techniques to identify smoke is a low cost and convenient method. However, industrial smoke detection is a challenging task because industrial emission particles are often decay rapidly outside the stacks or facilities and steam is very similar to smoke. To overcome these problems, a novel Spatio-Temporal Cross Network (STCNet) is proposed to recognize industrial smoke emissions. The proposed STCNet involves a spatial pathway to extract texture features …
AURA: A Hybrid Spatiotemporal-Chromatic Framework for Robust, Real-Time Detection of Industrial Smoke Emissions
Department of Integrative Systems and Design, Hong Kong University of Science and Technology（整合系统与设计系，香港科技大学）；AI Innovation Hub, ANTEI Limited（人工智能创新中心，ANTEI有限公司）；Institute of Applied Computer Science, ITMO University（应用计算机科学研究院，ITMO大学）
As sensing technology proliferates and becomes affordable to the general public, there is a growing trend in citizen science where scientists and volunteers form a strong partnership in conducting scientific research including problem finding, data collection, analysis, visualization, and storytelling. Providing easy-to-use computational tools to support citizen science has become an important issue. To raise the public awareness of environmental science and improve the air quality in local areas, we are currently collaborating with a local community in monitoring and documenting fugitive emis…
University of Catania（卡塔尼亚大学）；XGD-XENIA s.r.l.（XGD-XENIA有限公司）；University of Hertfordshire（赫特福德大学）
Wearable cameras allow to collect images and videos of humans interacting with the world. While human-object interactions have been thoroughly investigated in third person vision, the problem has been understudied in egocentric settings and in industrial scenarios. To fill this gap, we introduce MECCANO, the first dataset of egocentric videos to study human-object interactions in industrial-like settings. MECCANO has been acquired by 20 participants who were asked to build a motorbike model, for which they had to interact with tiny objects and tools. The dataset has been explicitly labeled for…
University of St. Gallen（圣加仑大学）；Sociovestix Labs Ltd.（Sociovestix实验室有限公司）；ISS Inc.（ISS公司）
The major driver of global warming has been identified as the anthropogenic release of greenhouse gas (GHG) emissions from industrial activities. The quantitative monitoring of these emissions is mandatory to fully understand their effect on the Earth's climate and to enforce emission regulations on a large scale. In this work, we investigate the possibility to detect and quantify industrial smoke plumes from globally and freely available multi-band image data from ESA's Sentinel-2 satellites. Using a modified ResNet-50, we can detect smoke plumes of different sizes with an accuracy of 94.3%. …
Smoke detection has become a significant task in associated industries due to the close relationship between the petrochemical industry's smoke emission and its safety production and environmental damage. There are several production situations in the real industrial production environment, including complete combustion of exhaust gas, inadequate combustion of exhaust gas, direct emission of exhaust gas, etc. We discovered that the datasets used in previous research work can only determine whether smoke is present or not, not its type. That is, the dataset's category does not map to the real-w…
Towards automatic smoke detector inspection: Recognition of the smoke detectors in industrial facilities and preparation for future drone integration
Brno University of Technology（布尔诺理工大学）；VSB - Technical University of Ostrava（俄斯特拉发技术大学）；University of Ostrava（俄斯特拉发大学）；Mendel University in Brno（布尔诺孟德尔大学）
LIRMM；Université de Montpellier（蒙彼利埃大学）；CNRS（法国国家科学研究中心）；Technische Universiteit Delft（代尔夫特理工大学）
Intuitive user interfaces are indispensable to interact with the human centric smart environments. In this paper, we propose a unified framework that recognizes both static and dynamic gestures, using simple RGB vision (without depth sensing). This feature makes it suitable for inexpensive human-robot interaction in social or industrial settings. We employ a pose-driven spatial attention strategy, which guides our proposed Static and Dynamic gestures Network - StaDNet. From the image of the human upper body, we estimate his/her depth, along with the region-of-interest around his/her hands. The…
School of Computing, National University of Singapore（计算学院，新加坡国立大学）；Hangzhou Global Science and Technology Innovation Center, Zhejiang University（杭州全球科技创新中心，浙江大学）
Toward Generalizable Deep Learning Based Peatland Fire Detection via Walsh Hadamard Transform and Domain Adaptation
Department of Electrical and Computer Engineering University of Illinois Chicago（电气与计算机工程系伊利诺伊大学芝加哥分校）；Fire and Rescue Department of Malaysia（马来西亚消防与救援部门）；Dept. of Chemical and Environmental Engineering Faculty of Engineering Universiti Putra Malaysia（化学与环境工程系乌拉尔大学马来西亚分校）；International Tropical Forestry Programme Faculty of Tropical Forestry Universiti Malaysia Sabah（国际热带林业计划热带林业系马来西亚沙巴大学）；USDA Forest Service Pacific Wildland Fire Sciences Laboratory（美国农业部森林服务局太平洋野火科学实验室）
A New Image Quality Database for Multiple Industrial Processes
Faculty of Information Technology, Beijing University of Technology（北京工业大学信息学部）；School of Electronic & Information Engineering, Liaoning University of Technology（辽宁工业大学电子与信息工程学院）；Dalian University of Technology（大连理工大学）；Beijing Artificial Intelligence Institute（北京人工智能研究院）
Order Matters: On Parameter-Efficient Image-to-Video Probing for Recognizing Nearly Symmetric Actions
Institute for Artificial Intelligence, University of Stuttgart（人工智能研究所，斯图加特大学）；Intelligent Assistive Systems Lab, University of Hildesheim（智能辅助系统实验室，希尔德斯海姆大学）
