Road Degradation Survey Through Images by Drone

Leonardi, Giovanni; Barrile, Vincenzo; Palamara, Rocco; Suraci, Federica; Candela, Gabriele

doi:10.1007/978-3-319-92102-0_24

Cited by 14 publications

(6 citation statements)

References 8 publications

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“…The image analysis acquired by innovative and rapid systems such as drones is a useful method for road infrastructure managers. The goal of these innovative methods is certainly to reduce the time for identifying the surface pavement distresses, but above all to reduce the investigations costs (Leonardi et al, 2019). Applications of UAV for pavement condition and road distress monitoring have been reviewed and showed that the use of UAV is still in the development phase and not yet in practice (Outay et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

“…In this study, there are still some errors in interpretation results caused by the shadow factor being taken during shooting, and due to the low spatial resolution of the camera, the texture of the ground surface in the 3D model to be less visible. The use of drone for pavement distress was carried out by Leonardi (2019), 25 m height was taken to obtained a good picture. Later, black and white color interpretation using MATLAB was used to identify pavement distress from aerial photos (Leonardi et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

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Implementation of UAV for Pavement Functional Performance Assesment

Astor¹,

Utami²,

Nabesima³

et al. 2023

IJSCET

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Pavement conditions could be degraded throughout its service life. Hence, a pavement management system is needed to ensure pavement performance according to its design life. To support a reliable pavement, a pavement condition survey needs to be conducted most effective and practical way. One of the technologies used in pavement condition surveys is the Unmanned Aerial Vehicle (UAV) or known as drone. The use of UAVs for road maintenance will reduce the cost and time and with the 3D model, the accuracy level is at the centimeter level which indicates UAVs are an excellent and promising tool for road work.In this study, a PCI method of pavement condition evaluationwill be used to asses pavement through both manually surveyed and 3D model calculation with the use of Agisoft application. Later on, statistical test will be carried out such as ANOVA and correlation to determine the relationship between pavement condition obtained from two different survey method as well as the comparison in identifying pavement distresss, PCI value and pavement condition. In result, those two methods can identify identical types of damage, with a fairly high percentage of > 60%. While, manual method could identify a higher percentage of degree of severity than 3D model with the help of application. Themanual of pavement condition assessment can identify in more detail than the Agisoft Metashape application. Based on statistical tests,the two methods used show a close and unidirectional relationship.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Implementation of UAV for Pavement Functional Performance Assesment

Astor¹,

Utami²,

Nabesima³

et al. 2023

IJSCET

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“…The black color shows cracks and potholes in the road. It was concluded that the drone method reduces economic costs and time to get accurate results [8]. Iradaf (2020) uses UAVs to identify and classify pavement distress, as well as calculate the extent of pavement distress.…”

Section: Introductionmentioning

confidence: 99%

3D Model of Pavement Distress Based on Road Gradient Using Unmanned Aerial Vehicle

Astor¹,

Utami²,

Winata³

et al. 2022

Proceedings of the Conference on Broad Exposure to Science and Technology 2021 (BEST 2021)

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The implementation of UAVs in Indonesia on road infrastructure projects still widely used at the planning stage of new road routes and the supervision stage to determine the progress of the work. Meanwhile, at the post-work monitoring stage, the UAV is only used to identify and inventory the pavement distress in determining the type of damage, not followed by the further pavement assessment. This study aims to determine the type of damage and the dimensions (area and depth) of the damage and determine the level of pavement distress. Data acquisition was carried out by taking direct photos using a drone of some damage to the road segment based on different road gradients in the study location, which was a 1% -6% slope. Photos taken from the drone are processed according to the rules of photogrammetry to produce an orthophoto as a 3D model. Furthermore, identification and inventory of types of pavement distress are carried out, as well as calculating the dimensions (area and depth) of pavement distress on photo maps. A minimum value difference of 0.3cm to 3cm is obtained from the calculation of the dimensions of the pavement distress in the model against the results of measurements in the field. The level of precision of the dimensions of the pavement distress in the model is not affected by the gradient of the road, meaning that the 3D model does not make the value of the depth of the pothole on a high road gradient more precise.

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“…The decreasing amount of forest road deteriorations and the identification of abnormal events are considered an important problem that has to be solved [2]. In order to attain these goals, smart systems need to be developed for monitoring and understanding our surroundings using images, videos, sensors, or depth cameras [3][4][5][6][7][8][9][10][11][12][13]. Moreover, to be economical and widely deployable, this deterioration detector must operate on embedded processors that dissipate far less power than powerful GPUs (Graphics Processing Unit) used for benchmarking in typical computer vision experiments [14].…”

Section: Introductionmentioning

confidence: 99%

Forest Roads Damage Detection Based on Objected Detection Deep Learning Algorithms

Heidari

Najafi

Borges

2022

Preprint

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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.

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Road Degradation Survey Through Images by Drone

Cited by 14 publications

References 8 publications

Implementation of UAV for Pavement Functional Performance Assesment

Implementation of UAV for Pavement Functional Performance Assesment

3D Model of Pavement Distress Based on Road Gradient Using Unmanned Aerial Vehicle

Forest Roads Damage Detection Based on Objected Detection Deep Learning Algorithms

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