Due to the high cost of identifying and evaluating forest roads and the subsequent issue of maintenance, forest management has always faced challenges in terms of scheduling and implementation costs. Currently, monitoring the condition of forest roads has reached a critical stage due to budget allocation problems and lack of adequate supervision and 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 assessment forest road dataset to run an experiment, smartphone images by various aspects of the smartphone images (SI) dataset which is specialized for detect forest road deterioration. To enhance YOLO's ability to detect damaged scenes by proposed 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.

In the recent decade, forest roads with a gravel surface course constructed in the different climates of Hyrcanian zone have worn out due to maintenance problems. This study was done to investigate the effect of traffic, maintenance budget, and climate on deterioration of forest roads and determine the best time and type of maintenance activities in the Mediterranean, sub-humid and semi arid climates. Unmanned aerial vehicle (UAV) was used to monitoring Unpaved Road Condition Index (UPCI), immediately after maintenance activities and each season for one year. Moreover, deterioration time of the wearing course was predicted using Monte Carlo time series analysis in MATLAB. Results showed that sub-humid climate presented lower UPCI (7.19) compared to the two other climates. UPCI values for Mediterranean and semi-arid climates were 7.81 and 8.82, respectively. In most cases, where roads were maintained by high-budget strategy, deterioration time was longer than other strategies, but cost-effectiveness (CE) value of low-budget strategy was more than other strategies in all traffic levels of Mediterranean climate and high-traffic roads in semi-arid climate. Low-budget maintenance activities include one culvert improvement per 6 km, light blading, 30 mm layer gravelling. In semi-arid climate, medium-budget maintenance strategy was more efficient in medium and low-traffic roads. In, sub-humid climates it was detected that CE values severely vary depending on the level of traffic. Medium, high and low budget maintenance strategies were respectively efficient in high, medium and low-traffic roads. High budget maintenance activities include one culvert improvement per 4 km, heavy blading and local compaction, 60 mm layer gravelling. Overall, it was concluded that monitoring UPCI over time and probability analysis using time series is useful for sustainable and long term management of forest roads.