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Recent Trends in Applications of Computer Vision in the Development of a Sustainable Environment

A special issue of Sustainability (ISSN 2071-1050). This special issue belongs to the section "Environmental Sustainability and Applications".

Deadline for manuscript submissions: closed (30 September 2023) | Viewed by 2426

Special Issue Editors


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Guest Editor
Department of Computer & System Sciences, Siksha Bhavana (Institute of Science) Visva-Bharati, Santiniketan 731235, India
Interests: applications in sustainable improvement; steganography; image processing; IoT

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Guest Editor
Department of Computer Science and Engineering, Kalyani Government Engineering College, Kalyani 741245, India
Interests: blockchain; sustainable development; IoT; cloud; steganography

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Guest Editor
Department of Computer Science and Engineering, Kalyani Government Engineering College, Kalyani 741245, India
Interests: applications in sustainable improvement; cloud computing; edge and fog computing; blockchain; IoT

Special Issue Information

Dear Colleagues,

The key feature of this Special Issue is to incorporate various profound technical aspects of computer vision for the achievement of sustainable development. Computer vision is a field involving endowing a machine with the ability to “see”, having gained interest in a wide range of research areas in recent years. This technology uses a camera and computer instead of the human eye to identify, track, and measure targets for further image processing, with the emergence of artificial intelligence having great potential for seeing, classifying, and processing visual data. Its engineering goal is to understand and automate human visual system tasks. The evolution of computer vision dramatically impacts the environment, society, and economy, and is considered a core sustainability issue. This also highlights the functionality and importance of green data, IoT, and blockchain in computer vision.

This Special Issue aims to attract many eminent scholars, researchers, and scientists to work in a field of such high interest, integrating different technologies, such as blockchain, IoT, big data, cloud computing, deep learning, machine learning, and smart grid for this computer-vision-assisted sustainable development.

Prof. Dr. Paramartha Dutta
Dr. Kousik Dasgupta
Dr. Sourav Banerjee
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sustainability is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • computer vision
  • sustainable development
  • IoT
  • blockchain
  • cloud computing

Published Papers (1 paper)

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Research

18 pages, 13583 KiB  
Article
Global Relation-Aware-Based Oil Detection Method for Water Surface of Catchment Wells in Hydropower Stations
by Jiajun Liu, Haokun Lin, Yue Liu, Lei Xiong, Chenjing Li, Tinghu Zhou and Mike Ma
Sustainability 2023, 15(8), 6966; https://doi.org/10.3390/su15086966 - 21 Apr 2023
Cited by 1 | Viewed by 1240
Abstract
The oil in hydropower station catchment wells is a source of water pollution which can cause the downstream river to become polluted. Timely detection of oil can effectively prevent the expansion of oil leakage and has important significance for protecting water sources. However, [...] Read more.
The oil in hydropower station catchment wells is a source of water pollution which can cause the downstream river to become polluted. Timely detection of oil can effectively prevent the expansion of oil leakage and has important significance for protecting water sources. However, the poor environment and insufficient light on the water surface of catchment wells make oil pollution detection difficult, and the real-time performance is poor. To address these problems, this paper proposes a catchment well oil detection method based on the global relation-aware attention mechanism. By embedding the global relation-aware attention mechanism in the backbone network of Yolov5s, the main features of oil are highlighted and the minor information is suppressed at the spatial and channel levels, improving the detection accuracy. Additionally, to address the problem of partial loss of detail information in the dataset caused by the harsh environment of the catchment wells, such as dim light and limited area, single-scale retinex histogram equalization is used to improve the grayscale and contrast of the oil images, enhancing the details of the dataset images and suppressing the noise. The experimental results show that the accuracy of the proposed method achieves 94.1% and 89% in detecting engine oil and turbine oil pollution, respectively. Compared with the Yolov5s, Faster R-CNN, SSD, and FSSD detection algorithms, our method effectively reduces the problems of missing and false detection, and has certain reference significance for the detection of oil pollution on the water surface of catchment wells. Full article
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