Topographically Guided UAV for Identifying Tension Cracks Using Image-Based Analytics in Open-Pit Mines

Winkelmaier, Garrett; Battulwar, Rushikesh; Khoshdeli, Mina; Valencia, Jorge Andrés Alvarado; Sattarvand, Javad; Parvin, Bahram

doi:10.1109/tie.2020.2992011

Cited by 16 publications

(9 citation statements)

References 26 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…These paths can be computed for detecting and monitoring tension cracks on mine benches or for measuring the positional accuracy of drill holes on a production bench using a drone. The centerline of benches in an open-pit mine can be automatically extracted from the DEM, as shown in Winkelmaier et al [11]. Wind speed during simulation studies was 0 m/s, and hence, not considered, although speed and direction of the wind are parameters in Equation (6).…”

Section: Discussionmentioning

confidence: 99%

See 1 more Smart Citation

A Practical Methodology for Generating High-Resolution 3D Models of Open-Pit Slopes Using UAVs: Flight Path Planning and Optimization

et al. 2020

Self Cite

View full text Add to dashboard Cite

High-resolution terrain models of open-pit mine highwalls and benches are essential in developing new automated slope monitoring systems for operational optimization. This paper presents several contributions to the field of remote sensing in surface mines providing a practical framework for generating high-resolution images using low-trim Unmanned Aerial Vehicles (UAVs). First, a novel mobile application was developed for autonomous drone flights to follow mine terrain and capture high-resolution images of the mine surface. In this article, case study is presented showcasing the ability of developed software to import area terrain, plan the flight accordingly, and finally execute the area mapping mission autonomously. Next, to model the drone’s battery performance, empirical studies were conducted considering various flight scenarios. A multivariate linear regression model for drone power consumption was derived from experimental data. The model has also been validated using data from a test flight. Finally, a genetic algorithm for solving the problem of flight planning and optimization has been employed. The developed power consumption model was used as the fitness function in the genetic algorithm. The designed algorithm was then validated using simulation studies. It is shown that the offered path optimization can reduce the time and energy of high-resolution imagery missions by over 50%. The current work provides a practical framework for stability monitoring of open-pit highwalls while achieving required energy optimization and imagery performance.

show abstract

Section: Discussionmentioning

confidence: 99%

“…It generates a series of flying waypoints at a constant, short distance above the ground to take high-resolution images. Images are processed by photogrammetry software to generate three-dimensional (3D) models of the pit, which are used for detecting and monitoring tension cracks [11].…”

Section: Introductionmentioning

confidence: 99%

A Practical Methodology for Generating High-Resolution 3D Models of Open-Pit Slopes Using UAVs: Flight Path Planning and Optimization

et al. 2020

Self Cite

View full text Add to dashboard Cite

show abstract

“…Due to the random distribution and wide space characteristics of pavement cracks, this detection method is very labor-intensive and requires many material resources, and the dangerous sections need to be tightly sealed when collecting and detecting in the field, which causes a certain degree of traffic safety hazards; therefore, it is difficult to achieve automatic pavement crack detection using the above traditional artificial pavement crack detection technology [4]. Now, there are also many domestic and foreign scholars studying pavement crack identification methods, such as Winkelmaier et al, who put forward the method of terrain-guided UAV to identify tension cracks in open pit mines [5]. The method uses a UAV to collect ground crack images; the UAV flies at a constant altitude according to a flight path preprogrammed to generate mosaic and depth-map images.…”

Section: Introductionmentioning

confidence: 99%

An Improved Crack Identification Method for Asphalt Concrete Pavement

Yang

2023

Applied Sciences

View full text Add to dashboard Cite

The results of high-precision asphalt concrete pavement crack identification can provide help for pavement maintenance. Therefore, methods of image feature enhancement and crack identification of asphalt concrete pavement cracks are proposed. First of all, we used an industrial CCD camera mounted on a vehicle to collect an asphalt concrete pavement crack image. Then, after using the NeighShrink algorithm to denoise the acquired image, a fractional differential image enhancement algorithm was designed based on image feature blocks to enhance the image features. On this basis, crack characteristics were segmented and processed by watershed algorithm. Through crack direction identification and crack parameter extraction, crack distribution direction, crack length and width and other parameters of asphalt concrete pavement were obtained in order to achieve accurate identification of asphalt concrete pavement cracks. The experiment found that this method can effectively remove noise information from asphalt concrete crack images; after applying this method, the image entropy value of each image was improved, with a minimum improvement of 0.38 and a maximum improvement of 1.98. The time consumed by this method in identifying cracks in asphalt concrete pavement varied between 1.4 s and 2.4 s. When identifying the length of cracks in asphalt concrete pavement, the maximum deviation value was only 0.47 mm; when identifying the width of cracks in asphalt concrete pavement, the maximum deviation value was only 0.31 mm. The above results indicate that by enhancing the image features of asphalt concrete cracks, this method achieves more accurate identification results for crack distribution direction, length and width values, with high identification efficiency and good application effect.

show abstract

“…Over the last decade, Light Detection and Ranging (LIDAR) (e.g., Riquelme et al, 2014;Singh et al, 2021;Chen et al, 2017) and optical photogrammetric methods (e.g., Bogdanowitsch et al, 2022, Winkelmaier et al, 2020 have been widely used in the detection of rock mass discontinuities. Although to LiDAR technology has the capability to produce accurate models, it has high costs in terms of hardware, software and computation, which negatively affect its widespread use.…”

Section: Introductionmentioning

confidence: 99%

A CNN Architecture for Discontinuity Determination of Rock Masses With Close Range Images

Yalcin

Can

Kocaman

et al. 2022

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

View full text Add to dashboard Cite

Abstract. Determination of discontinuities in rock mass requires scan-line surveys performed in in-situ that can reach up to dangerous and challenging dimensions. With the development of novel technological equipments and algorithms, the studies related to rock mass discontinuity determination remain up-to-date. Depending on the development of the Structure from Motion (SfM) method in the field of close-range photogrammetry, low-cost cameras can be used to produce 3D models of rock masses. However, the determination of rock mass discontinuity parameters must still be carried out manually on these models. Within the scope of this study, a Convolutional Neural Network (CNN) architecture is proposed to identify the discontinuities automatically as the first step for fully automated processing. The Kızılcahamam/Güvem Basalt Columns Geosite near Ankara, Turkey was determined as the study area. The orthophoto of this study area was produced using close-range photogrammetric methods and the training data was produced by manual mensuration. The dataset consists of labeled binary masks and images containing corresponding Red-Green-Blue (RGB) bands. Furthermore, the amount of data was increased by applying augmentation methods to the dataset. The U-Net architecture was used to detect rock discontinuities based on the produced orthophoto. The preliminary results presented here reveal that the discontinuity determination capability of the proposed method is high based on the visual assessments, while problems exist with image quality and discontinuity identification. In addition, considering the small size of the training dataset, the accuracy of the model would increase when a larger dataset could be employed.

show abstract

Topographically Guided UAV for Identifying Tension Cracks Using Image-Based Analytics in Open-Pit Mines

Cited by 16 publications

References 26 publications

A Practical Methodology for Generating High-Resolution 3D Models of Open-Pit Slopes Using UAVs: Flight Path Planning and Optimization

A Practical Methodology for Generating High-Resolution 3D Models of Open-Pit Slopes Using UAVs: Flight Path Planning and Optimization

An Improved Crack Identification Method for Asphalt Concrete Pavement

A CNN Architecture for Discontinuity Determination of Rock Masses With Close Range Images

Contact Info

Product

Resources

About