## 5 Conclusions  
In the present work, a deep learning convolutional neural network model based on several existing architectures of image classifiers was obtained using fine-tuning. The data for fine-tuning were carefully selected from thousands of existing orthoframes freshly provided by the company and having better image quality compared to the images used in [2].  
The manual preprocessing step that included digitizing the orthoframes, i.e., manually painting defect masks and updating the road mask by eliminating image areas with poor sharpness and also areas outside the pavement part, while time consuming and tedious, was proven to be critical for the success of the implementation of the detector. In previous work, we used the data provided by the company for generating ground truth information. However, it must be taken into account that the internal purposes of digitization in the company were different, so pixel annotation accuracy was not the most important factor. Therefore, to ensure that the detection model was developed based on relevant information, a more accurate localization of defects had to be introduced. Due to this redundant approach in annotating images, the proposed solution while not completely foolproof should be fairly robust with respect to annotation mistakes, at least from the point of view of visual inspection.  
Furthermore, data augmentation was proven to be useful to combat differing lighting conditions that still presented a challenge while analyzing the image. The next step for data augmentation is the implementation of distortion tuning [71].  
Instead of attempting to train convolutional neural networks from scratch, we only considered pretrained neural networks in this work. The reason for this was that significantly better results were obtained with pretrained networks compared to the results reported in [2], and therefore, using simpler network structures was not considered a benefit. Indeed, precision and recall metrics increased from 0.22 and 0.35, respectively, to 0.90 and 0.87. This was a significant improvement and was very likely related to several factors, including better quality orthoframes, using only sharp image areas, andmanually digitizing the images, annotating defects and updating pavement masks as needed. The latter also contributed to solving the problem that appeared in [2] where the majority of false positive detections was due to the classifier incorrectly identifying road edges as pavement distress.  
Finally, the software package proposed to the company was updated and also included an efficient image annotation tool tailored to the specific purpose of preparing higher quality ground truth files for defect detection and pavement area extraction. Although a different deep learning backend was used (PyTorch and FastAI instead of TensorFlow and Keras), the software is easy to update and hence it will soon be ready for deployment, further testing, and its eventual application for improving highway road pavement conditions.  
All authors worked on this research together and approved the paper.  
This research endeavor was partially supported by the Archimedes Foundation and Reach-U Ltd. in the scope of the smart specialization research and development project #LEP19022: \"Applied research for creating a cost-effective interchangeable 3D spatial data infrastructure with survey-grade accuracy\".  
The authors declare no conflict of interest.