Drvna industrija, Vol. 73 No. 2, 2022.
Izvorni znanstveni članak
https://doi.org/10.5552/drvind.2022.2108
Automatic Damage Detection on Traditional Wooden Structures with Deep Learning-Based Image Classification Method
Kemal Haciefendioglu
; Karadeniz Technical University, Department of Civil Engineering, Trabzon, Turkey
Hasan Basri Başaga
; Karadeniz Technical University, Department of Civil Engineering, Trabzon, Turkey
Murat Emre Kartal
; İzmir Democracy University, Department of Civil Engineering, İzmir, Turkey
Mehmet Ceyhun Bulut
; Karadeniz Technical University, Department of Civil Engineering, Trabzon, Turkey
Sažetak
Wood has a long history of being used as a valuable resource when it comes to building materials. Due to various external factors, in particular the weather, wood is liable to progressive damage over time, which negatively impacts the endurance of wooden structures. Damage assessment is key in understanding, as well as in effectively mitigating, problems that wooden structures are likely to face. The use of a classification system, via deep learning, can potentially reduce the probability of damage in engineering projects reliant on wood. The present study employed a transfer learning technique, to achieve greater accuracy, and instead of training a model from scratch, to determine the likelihood of risks to wooden structures prior to project commencement. Pretrained MobileNet_V2, Inception_V3, and ResNet_V2_50 models were used to customize and initialize weights. A separate set of images, not shown to the trained model, was used to examine the robustness of the models. The three models were compared in their abilities to assess the possibilities and types of damage. Results revealed that all three models achieve performance rates of similar reliability. However, when considering the loss ratios in regard to efficiency, it became apparent that the multi-layered MobileNet_V2 classifier stood out as the most effective of the pre-trained deep convolutional neural network (CNN) models.
Ključne riječi
deep learning method; convolutional neural networks; MobileNet_V2; Inception_V3; ResNet_ V2_50; wooden structures
Hrčak ID:
278445
URI
Datum izdavanja:
31.5.2022.
Posjeta: 985 *