Automatic Concrete Surface Crack Recognition Using EfficientNetV2 Variants
- Resource Type
- Conference
- Authors
- Rama, A.; Damasevicius, Robertas; Arunmozhi, S.; Mohammed, Mazin Abed; Hussam, Ragheed; Rajinikanth, V.
- Source
- 2023 International Conference on System, Computation, Automation and Networking (ICSCAN) System, Computation, Automation and Networking (ICSCAN), 2023 International Conference on. :1-5 Nov, 2023
- Subject
- Communication, Networking and Broadcast Technologies
Components, Circuits, Devices and Systems
Computing and Processing
Engineered Materials, Dielectrics and Plasmas
Fields, Waves and Electromagnetics
General Topics for Engineers
Photonics and Electrooptics
Power, Energy and Industry Applications
Robotics and Control Systems
Signal Processing and Analysis
Training
Deep learning
Buildings
Feature extraction
Digital cameras
Safety
Classification algorithms
Structural crack
EfficientNetV2
fused features
classification
improved accuracy
- Language
Building health and structural integrity are important to ensure occupant safety and long-term durability. Monitoring and assessing building condition regularly ensures that potential problems are identified early. This research aims to develop and implement a deep-learning (DL) supported tool to examine the concrete surface images collected using digital camera. The data gathering forms the first stage and the chosen DL algorithm is used for feature extraction followed by the feature selection and then fusion is done and finally a fivefold cross verification schema is applied for a binary classification. This work considered the EfficientNetV2 (ENV2) model with variants like small, medium and large for the examination. The investigation is implemented using individual and fused deep-features and the performance of the developed tool is confirmed using the achieved accuracy. The outcome of this research confirms that the proposed tool produces a betterment of >96% accuracy with individual-features and 100% accuracy with fused-features. These results confirm that the proposed scheme provides significant result during the crack detection.