| 요약1 |
이 연구의 목적은 콘크리트 균열 탐지를 위한 딥 러닝(Deep Learning) 기반 Convolution Neural Network(CNN)의 모델을 비교하는 것이다. CNN 모델로 사용된 네트워크는 ImageNet Large Scale Visual Recognition Challenge(ILSVRC)에서 뛰어난 성과를 거둔 AlexNet, GoogLeNet, VGG16, VGG19, ResNet-18, ResNet-50, ResNet-101, SqueezeNet이 사용되었다. |
| 요약2 |
The purpose of this study is to compare the models of Deep Learning-based Convolution Neural Network(CNN) for concrete crack detection.
The comparison models are AlexNet, GoogLeNet, VGG16, VGG19, ResNet-18, ResNet-50, ResNet-101, and SqueezeNet which won ImageNet
Large Scale Visual Recognition Challenge(ILSVRC). To train, validate and test these models, we constructed 3000 training data and 12000
validation data with 256×256 pixel resolution consisting of cracked and non-cracked images, and constructed 5 test data with 4160×3120
pixel resolution consisting of concrete images with crack. In order to increase the efficiency of the training, transfer learning was performed
by taking the weight from the pre-trained network supported by MATLAB. From the trained network, the validation data is classified into
crack image and non-crack image, yielding True Positive (TP), True Negative (TN), False Positive (FP), False Negative (FN), and 6
performance indicators, False Negative Rate (FNR), False Positive Rate (FPR), Error Rate, Recall, Precision, Accuracy were calculated. The
test image was scanned twice with a sliding window of 256×256 pixel resolution to classify the cracks, resulting in a crack map. From the
comparison of the performance indicators and the crack map, it was concluded that VGG16 and VGG19 were the most suitable for detecting
concrete cracks. |