A Newly Adopted YOLOv9 Model for Detecting Mould Regions Inside of Buildings

Mansouri, Taha; Mashuk, Md. Shadab; Palaiahnakote, Shivakumara; Chacko, Aaron; Sykes, Lawrence; Alameer, Ali

Abstract

Molds on wall and ceiling surfaces in damp indoor environments especially in houses with poor insulation and ventilation are common in the UK. Since it releases toxic chemicals as it grows, it is a serious health hazard for occupants who live in such houses. For example, eye irritation, sneezing, nose bleeds, respiratory infections, and skin irritations. Furthermore, there are chances of developing serious medical conditions like lung infections and respiratory diseases which may even lead to death. The main challenge here is that due to their irregular patterns, camouflaged with the background, it is not so easy to detect with our naked eyes in the early stage and often confused as stains. Therefore, inspired by the accomplishments of the Yolo architecture for object detection, the Yolov9 model is explored for mold detection by considering mold region as an object in this work. The overall result shows a promising 76% average classification rate. Since the mold does not have a shape, specific pattern, or color, adapting the Yolov9 for accurate mold detection is challenging. To the best of our knowledge, this is the first of its kind compared to existing methods. Since it is the first work, we constructed a dataset to perform experiments and evaluate the proposed method. To demonstrate the proposed method’s effectiveness, the results were also compared with the results of the Yolov8 and Yolov10 models.

• Journal Article Type: Article
• Acceptance Date: Oct 28, 2024
• Online Publication Date: Jan 24, 2025
• Publication Date: Nov 15, 2024
• Deposit Date: Oct 29, 2024
• Publicly Available Date: Nov 15, 2024
• Journal: International Journal of Pattern Recognition and Artificial Intelligence
• Print ISSN: 0218-0014
• Electronic ISSN: 1793-6381
• Publisher: World Scientific Publishing
• Peer Reviewed: Peer Reviewed
• DOI: https://doi.org/10.1142/s0218001424500253
• Keywords: Mould; Computer Vision; Deep Learning; Indoor Health; Mould detection; Mould classification