| Title |
A Study on Optimal Convolutional Neural Networks Backbone for Reinforced Concrete Damage Feature Extraction |
| DOI |
https://doi.org/10.12652/Ksce.2023.43.4.0511 |
| Keywords |
철근콘크리트 손상 이미지; 무인항공기; 특성 추출; 컨볼루션 신경망; 모바일 Reinforced concrete damage image; Unmanned aerial vehicle; Feature extraction; Convolution neural network; MobileNets |
| Abstract |
'Research on the integration of unmanned aerial vehicles and deep learning for reinforced concrete damage detection is actively underway. Convolutional neural networks have a high impact on the performance of image classification, detection, and segmentation as backbones. The MobileNet, a pre-trained convolutional neural network, is efficient as a backbone for an unmanned aerial vehicle-based damage detection model because it can achieve sufficient accuracy with low computational complexity. Analyzing vanilla convolutional neural networks and MobileNet under various conditions, MobileNet was evaluated to have a verification accuracy 6.0~9.0% higher than vanilla convolutional neural networks with 15.9~22.9% lower computational complexity. MobileNetV2, MobileNetV3Large and MobileNetV3Small showed almost identical maximum verification accuracy, and the optimal conditions for MobileNet's reinforced concrete damage image feature extraction were analyzed to be the optimizer RMSprop, no dropout, and average pooling. The maximum validation accuracy of 75.49% for 7 types of damage detection based on MobilenetV2 derived in this study can be improved by image accumulation and continuous learning. |