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Optimization of A Pavement Defects Detection System Using A Deep Learning Algorithm with Pre-train
Abstract
Detecting defects in road pavement is a highly practical problem and of great research interest in the field of computer vision. In recent years, Deep Learning has made many important breakthroughs in object detection and recognition. In this paper, we apply a Deep Learning method using VGG16 and R-CNN Region Convolution Neural Network architectures trained on the individual HL88 dataset. The performance of the network is evaluated on the Portuguese and Russia datasets to detect crack types. The Russia dataset provides the best results for the R-CNN architecture using weights trained on a large dataset of sidewalk defects, achieving an accuracy of 96.5% and an F1 score of 97.9% in the test
set.
Keywords
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