Minor Surface Cracks Detection using SCD11 Convolutional Neural Network

Adil Hussain, Kashif Naseer Qureshi, Faizan Zaman, Ayesha Aslam, Tariq

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

The manual detection of road cracks is a time-consuming process. On the other hand, solutions that are based on deep learning are both speedy and accurate. Recently, several different Convolutional Neural Networks (CNN) based on deep learning have been proposed. However, the performance of the CNN models has varied. The major challenge is the computational resources required to train a pre-trained CNN model; however, a lightweight CNN is more suitable for better training efficiency. In this study work, the SCD11 CNN model is implemented and compared with the pre-trained CNN models, including Inception V2, VGG19, and Xception CNN. The models are trained and tested using the public dataset i.e., the Surface Cracks Dataset. The dataset is divided into training, validation and test sets. The SCD11 CNN along with the pre-trained CNN models are trained and validated and then tested using the splitting of the public dataset. Furthermore, the model's performance evaluation is performed by using a private dataset. The results show that the SCD11 CNN performs better than the pre-trained CNN models for both the public and private datasets.

Original languageEnglish
Title of host publication4th International Conference on Emerging Smart Technologies and Applications, eSmarTA 2024
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9798350354133
DOIs
Publication statusPublished - 2024
Event4th International Conference on Emerging Smart Technologies and Applications, eSmarTA 2024 - Sana'a, Yemen
Duration: 6 Aug 20247 Aug 2024

Publication series

Name4th International Conference on Emerging Smart Technologies and Applications, eSmarTA 2024

Conference

Conference4th International Conference on Emerging Smart Technologies and Applications, eSmarTA 2024
Country/TerritoryYemen
CitySana'a
Period6/08/247/08/24

Keywords

  • Cracks Detection
  • Deep Learning
  • Minor Cracks
  • Road Cracks
  • SCD11 CNN

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