UAVs in rail damage image diagnostics supported by deep-learning networks

Bojarczak, Piotr; Lesiak, P.

doi:10.1515/eng-2021-0033

Cited by 20 publications

(6 citation statements)

References 19 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Ni et al [43] experimentally performed rail region extraction, edge detection, detect contour filling in their proposed study to detect rail surface defects. Bojarczak and Lesiak [44] used a deep learning network implemented in the Tensorflow environment, such as FCN-8, to experimentally prevent the brightness of the images from affecting the segmentation success in their proposed study to detect rail surface defects. Thus, they were able to perform image segmentation with a success rate of 81%.…”

Section: Referencesmentioning

confidence: 99%

A New Rail Surface Defects Detection Approach Using 3D Laser Cameras Based on ResNet50

Santur¹,

Yilmazer²,

Karaköse³

et al. 2022

View full text Add to dashboard Cite

Rail transportation systems, which are used as one of the most common means of transportation worldwide, should be regularly inspected to prevent accidents that may occur. The rail condition monitoring can be performed in high accuracy and real time using computer vision, deep learning algorithms today. In this study, a new deep learning based approach using 3D laser cameras for rail inspection is presented. In the proposed approach, two 3D laser cameras placed on a real train, seeing the rail line from the left and right surfaces were used. These data consisting of sensitive distance value constitute the input data of the ResNet50 transfer learning model. The training was carried out on Nvidia Cuda supported graphics processing units using ResNet50 Convolutional Neural Network. During the test phase, the operation speed and accuracy rate of the method was measured by repeating the process on real-time rail profiles. The accuracy rate was calculated as 94%. As a result a new approach is presented based on deep learning using 3D laser cameras for rail inspection is presented.

show abstract

Section: Referencesmentioning

confidence: 99%

A New Rail Surface Defects Detection Approach Using 3D Laser Cameras Based on ResNet50

Santur¹,

Yilmazer²,

Karaköse³

et al. 2022

View full text Add to dashboard Cite

show abstract

“…They have trained the images on several one-stage models and inferred that a model RetinaNet can detect road damages with high accuracy. Bojarczak et al (2021) proposed a model damage diagnosis of railways using a deep learning model and UAVs. They used a fully convoluted network (I.e., semantic segmentation) to locate the railhead's defects.…”

Section: Road Condition Assessmentmentioning

confidence: 99%

Automatic assessment of roofs conditions using artificial intelligence (AI) and unmanned aerial vehicles (UAVs)

Alzarrad

Awolusi²,

Hatamleh³

et al. 2022

Front. Built Environ.

View full text Add to dashboard Cite

Building roof inspections must be performed regularly to ensure repairs and replacements are done promptly. These inspections get overlooked on sloped roofs due to the inefficiency of manual inspections and the difficulty of accessing sloped roofs. Walking a roof to inspect each tile is time-consuming, and as the roof slope increases, this difficulty increases the time needed for an inspection. Moreover, there is an intrinsic safety risk involved. Falls from roofs tend to cause severe and expensive injuries. The emergence of new sensing technologies and artificial intelligence (AI) such as high-resolution imagery and deep learning has enabled humans to move beyond the concept of using manual labor in damage assessments. It has brought significant advantages in the field of safety management, and it can be a substitute for the traditional assessment of roofs. This study uses unmanned aerial vehicles (UAVs) and deep learning technology to perform sloped roof inspections effectively, thus eliminating the safety risk involved in traditional manual inspections. This study utilizes UAVs and deep learning to automatically collect and classify roof imagery to identify missing shingles on the roof. The proposed research can help real estate agents, insurance companies, and others make better and more informed decisions about roof conditions. Future research could be refining the model to deal with different types of defects in addition to missing shingles.

show abstract

“…The introduction of cost-effective nonmetric camera-equipped unmanned aerial vehicles (UAV), along with the development of structure from motion (SfM) software, has recently become a convenient platform for surveyors to capture a range of diverse data upon the surface of the earth and aid in creating intricate georeferenced 3D digital elevation models (DEM) (Eltner & Schneider, 2015;Ouédraogo et al, 2014;Turner et al, 2012). There has been rapid adoption of these UAV systems in a variety of sectors, ranging from open-pit mining to pipeline and rail corridor inspections (Bojarczak & Lesiak, 2021;Park & Choi, 2020), given an often-emphasised potential for timesaving, ease of use and the DEM quality that can be achieved.…”

Section: Introductionmentioning

confidence: 99%

Investigation into Camera Calibration Flight Paths for UAV-Based Corridor Surveys

Henharen,

Helmholz

2024

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

View full text Add to dashboard Cite

Abstract. There is increasing adoption of cost-effective nonmetric camera-equipped unmanned aerial vehicles due to the perceived benefits of timesaving, ease of use, and the accuracy of the digital elevation models that can be produced using structure from motion software. The introduction of systematic elevation errors, doming and bowing, has been evidenced by several authors, and various methods have been identified to reduce these errors. This paper aims to analyse the impact of flight plans on these systematic errors using the especially challenging case of a corridor survey. Two sites were flown for the survey using a DJI Zenmuse. The first site, a car park, was utilised for on-the-job pre-calibration of the camera and consisted of several orbit flights and a double grip flight. Subsequently, an adjacent road (a corridor survey overall 428 m long) was also surveyed at 60 m and 80 m heights using varying flight configurations. This study confirms that pre-calibrating the camera's IOPs significantly reduces the root mean squared elevation error (from 0.268 m to 0.034 m) compared to self-calibrated IOPs using the corridor flights. The impact of flight design on elevation errors confirms a single flight path's risk and the benefits of two or more flight paths, including a point-of-interest orbit flight.

show abstract

UAVs in rail damage image diagnostics supported by deep-learning networks

Cited by 20 publications

References 19 publications

A New Rail Surface Defects Detection Approach Using 3D Laser Cameras Based on ResNet50

A New Rail Surface Defects Detection Approach Using 3D Laser Cameras Based on ResNet50

Automatic assessment of roofs conditions using artificial intelligence (AI) and unmanned aerial vehicles (UAVs)

Investigation into Camera Calibration Flight Paths for UAV-Based Corridor Surveys

Contact Info

Product

Resources

About