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CNN-Based Detection of Cracks and Moulds in Buildings

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Mobile Computing and Sustainable Informatics

Part of the book series: Lecture Notes on Data Engineering and Communications Technologies ((LNDECT,volume 166))

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Abstract

An exponential increase in population has created a high demand for housing. It is of paramount importance for stakeholders to maintain buildings and other mega-structures, to ensure their longevity. Building fault detection is a crucial step to address problems which might develop during construction or post completion. Detecting these faults early allows for corrective action to be taken immediately. However, this process is still being done manually, which is time-consuming, expensive, hazardous and provides room for human error. Deep learning is an efficient way to replace manual overseeing. The proposed solution involves using a deep learning model to accurately classify faults according to their types, and localize them. For this purpose, a web-scraped dataset of three categories, namely clean, crack and mould walls has been created. A comparison between three convolutional neural networks, including ResNet-50, Inception-v3 and VGG-16 is made, with ResNet-50 having the highest accuracy of 90.68%. Class Activation Mapping is used to identify and localize regions of faults. The metrics used also validate the robustness of the model, which would act as a prototype for a large-scale solution of building fault detection in the long run.

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Authors and Affiliations

  1. Vellore Institute of Technology, Chennai, India

    V. Maheysh & S. Kirthica

Authors
  1. V. Maheysh
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  2. S. Kirthica
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Corresponding author

Correspondence to S. Kirthica .

Editor information

Editors and Affiliations

  1. Pulchowk Campus, Tribhuvan University, Kathmandu, Nepal

    Subarna Shakya

  2. Department of Computer Science, International Hellenic University, Kavala, Greece

    George Papakostas

  3. Computer Science Department, Texas Southern University, Houston, TX, USA

    Khaled A. Kamel

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Maheysh, V., Kirthica, S. (2023). CNN-Based Detection of Cracks and Moulds in Buildings. In: Shakya, S., Papakostas, G., Kamel, K.A. (eds) Mobile Computing and Sustainable Informatics. Lecture Notes on Data Engineering and Communications Technologies, vol 166. Springer, Singapore. https://doi.org/10.1007/978-981-99-0835-6_52

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