Abstract
Poor road-surface conditions pose a significant safety risk to vehicle operation, especially in the case of autonomous vehicles. Hence, maintenance of road surfaces will become even more important in the future. With the development of deep learning-based computer image processing technology, artificial intelligence models that evaluate road conditions are being actively researched. However, as the lighting conditions of the road surface vary depending on the weather, the model performance may degrade for an image whose brightness falls outside the range of the learned image, even for the same road. In this study, a semantic segmentation model with an autoencoder structure was developed for detecting road surface along with a CNN-based image preprocessing model. This setup ensures better road-surface crack detection by adjusting the image brightness before it is input into the road-crack detection model. When the preprocessing model was applied, the road-crack segmentation model exhibited consistent performance even under varying brightness values.
Highlights
Electronics 2021, 10, 1402. https://To ensure driver safety and smooth vehicle operation, the road surface must always be maintained in a good condition
Computer vision problems based on deep learning can be classified into image classification [1,2,3,4], object detection [5,6,7], semantic segmentation [8,9], instance segmentation [10,11,12], and panoptic segmentation [13,14]
In this study, we developed a fully convolutional network (FCN) model that semantically divides the damaged road pixels on a road-surface image and a convolutional neural network (CNN) model that automatically provides an image-brightness-control variable to enable the developed FCN model to achieve the best detection performance
Summary
To ensure driver safety and smooth vehicle operation, the road surface must always be maintained in a good condition. Tane et al [26] developed a crack detector based on Mask R-CNN by training 352 original and annotated crack images It appears that there are an inadequate number of image datasets suitable for training instance segmentation models for the crack detection problem. Even for the same surface damage at the same location, if the external environmental changes and leads to a change in brightness levels with respect to the training images, the damage may not be detected To address this issue, in this study, we developed a fully convolutional network (FCN) model that semantically divides the damaged road pixels on a road-surface image and a CNN model that automatically provides an image-brightness-control variable to enable the developed FCN model to achieve the best detection performance.
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