TY - GEN
T1 - A Novel SCD11 CNN Model Performance Evaluation with Inception V3, VGG16 and ResNet50 Using Surface Crack Dataset
AU - Hussain, Adil
AU - Qureshi, Kashif Naseer
AU - Anwar, Raja Waseem
AU - Aslam, Ayesha
N1 - Publisher Copyright:
© 2024 IEEE.
PY - 2024
Y1 - 2024
N2 - Image processing techniques can be used to detect potential defects in images of infrastructure elements that have been collected or scanned. Aside from image processing, machine learning technologies are increasingly being applied to increase crack detection performance and resilience. A novel Surface Crack Detection Convolutional Neural Network (CNN) named the SCD11 CNN model is proposed. The main aim of this research paper is to improve efficiency and minimize the loss rate. The proposed CNN model is compared with the CNNs, including Inception V3, VGG16, and ResNet50, using a Surface Cracks Dataset. The accuracy and loss of the proposed model are compared. The results show that the proposed SCD11 CNN model performs better in test accuracy than Inception V3 and VGG16 and has a slightly lower accuracy than ResNet50.
AB - Image processing techniques can be used to detect potential defects in images of infrastructure elements that have been collected or scanned. Aside from image processing, machine learning technologies are increasingly being applied to increase crack detection performance and resilience. A novel Surface Crack Detection Convolutional Neural Network (CNN) named the SCD11 CNN model is proposed. The main aim of this research paper is to improve efficiency and minimize the loss rate. The proposed CNN model is compared with the CNNs, including Inception V3, VGG16, and ResNet50, using a Surface Cracks Dataset. The accuracy and loss of the proposed model are compared. The results show that the proposed SCD11 CNN model performs better in test accuracy than Inception V3 and VGG16 and has a slightly lower accuracy than ResNet50.
KW - cracks detection
KW - neural networks
KW - road cracks
UR - http://www.scopus.com/inward/record.url?scp=85189616379&partnerID=8YFLogxK
U2 - 10.1109/UVS59630.2024.10467149
DO - 10.1109/UVS59630.2024.10467149
M3 - Conference contribution
AN - SCOPUS:85189616379
T3 - 2nd International Conference on Unmanned Vehicle Systems-Oman, UVS 2024
BT - 2nd International Conference on Unmanned Vehicle Systems-Oman, UVS 2024
A2 - Al-Hashim, Aliya
A2 - Pervez, Tasneem
A2 - Khriji, Lazhar
A2 - Waris, Muhammad Bilal
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2nd International Conference on Unmanned Vehicle Systems-Oman, UVS 2024
Y2 - 12 February 2024 through 14 February 2024
ER -
