A computer vision model to detect black mold

   School of Science, Engineering and Environment

  Dr Taha Mansouri, Dr Ali Alameer  Applications accepted all year round  Self-Funded PhD Students Only

About the Project

Information on this PhD research area can be found further down this page under the details about the Widening Participation Scholarship given immediately below.

Applications for this PhD research are welcomed from anyone worldwide but there is an opportunity for UK candidates (or eligible for UK fees) to apply for a widening participation scholarship.

Widening Participation Scholarship: Any UK candidates (or eligible for UK fees) is invited to apply. Our scholarships seek to increase participation from groups currently under-represented within research. A priority will be given to students that meet the widening participation criteria and to graduates of the University of Salford. For more information about widening participation, follow this link: https://www.salford.ac.uk/postgraduate-research/fees. [Scroll down the page until you reach the heading “PhD widening participation scholarships”.] Please note: we accept applications all year but the deadline for applying for the widening participation scholarships in 2024 is 28th March 2024. All candidates who wish to apply for the MPhil or PhD widening participation scholarship will first need to apply for and be accepted onto a research degree programme. As long as you have submitted your completed application for September/October 2024 intake by 28 February 2024 and you qualify for UK fees, you will be sent a very short scholarship application. This form must be returned by 28 March 2024. Applications received after this date must either wait until the next round or opt for the self-funded PhD route.


Project description: The goal of this PhD project is to develop cutting-edge computer vision models that can effectively detect black mold in various environments. Black mold, or Stachybotrys chartarum, is a common type of mold that can pose a serious health risk to individuals exposed to it. The proposed computer vision models will take into account the conditions that enable mold growth and provide valuable information to building occupants and owners to identify mold growth before it becomes a major issue.

The project will focus on three main objectives: object detection, image classification, and semantic segmentation. Object detection involves locating and classifying mold growth in images, while image classification involves assigning images to different classes based on the presence or absence of mold. Semantic segmentation is the most challenging objective and involves accurately identifying and delineating mold regions in images.

The project will require the candidate to have a strong background in computer vision, machine learning, and image processing. They will be expected to conduct a thorough literature review of existing approaches for mold detection and segmentation, develop and implement novel computer vision models including YoLo, Faster RCNN, Mask RCNN and Denoising Diffusion Probabilistic Models, and test and validate these models on large-scale datasets.

In summary, this PhD project aims to design advanced computer vision models that can accurately detect black mold in various settings, providing valuable information to building occupants and owners to prevent health hazards and minimize property damage. The project will utilize state-of-the-art techniques and methodologies, and the successful candidate will have the opportunity to contribute to the development of a critical field with practical applications.

Architecture, Building & Planning (3) Computer Science (8)

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