| Title |
Comparison of Deep Learning-based CNN Models for Crack Detection |
| Authors |
Seol, Dong-Hyeon ; Oh, Ji-Hoon ; Kim, Hong-Jin |
| DOI |
https://doi.org/10.5659/JAIK_SC.2020.36.3.113 |
| Keywords |
Crack Detection; ILSVRC; Deep Learning; CNN; Transfer Learning |
| Abstract |
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. |