Forest roads damage detection based on deep learning algorithms

Currently, forest road monitoring reached a critical stage and need requires low-cost or cost-effective monitoring. Today, smartphones have been used on public roads to identify road deterioration due to benefits such as usability, cost, ease of access, and expected accuracy. The use of smartphones in forest road development by the proposed system is a distributed information system that converts data from enterprise mode to field mode by harvesting and assessing forest road conditions and image processing technologies. The technology proposed in this research allows different information YOLOv4-v5 with improvements to this version including mosaic data augmentation and automatic learning of enclosing frames. In this research, we applied a new hybrid YOLOv4-v5 to the dataset's general applicability. We assessed the forest road dataset to run an experiment, smartphone images by various aspects of the smartphone images (SI) dataset which is specialized for detecting forest road deterioration. To enhance YOLO's ability to detect damaged scenes by proposing a new technique that takes information into frames. We expanded the scope of the model by applying it to a new orientation estimation task. The main disadvantage is the provision of qualitative model information on forest road activity and the indication of potential deterioration.