Localization of an unmanned aerial vehicle for crack detection in railway tracks

Sushant, Shami; Anand, Sheila; James, Thomas W.; Aravind, Vishnu; Narayanan, Gayathri

doi:10.1109/icacci.2017.8126030

Cited by 9 publications

(5 citation statements)

References 6 publications

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“…Track defect identification: Mammeri et al (2021) evaluated the effectiveness of a method to segment tracks from drone images by using a fully convolutional encoder-decoder type segmentation network called U-Net from ING Robotic Aviation (Mammeri, Jabbar Siddiqui, & Zhao, 2021). Sushant et al (2017) proposed a localization method known as Monte Carlo or particle filter localization algorithm to identify fractures along the railway (Sushant, Anand, James, Aravind, & Narayanan, 2017).…”

Section: Defect Identificationmentioning

confidence: 99%

Systematic Literature Review of Drone Utility in Railway Condition Monitoring

Askarzadeh

Bridgelall

Tolliver

2023

J. Transp. Eng., Part A: Systems

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Drones have recently become a new tool in railway inspection and monitoring (RIM) worldwide, but there is still a lack of information about the specific benefits and costs. This study conducts a systematic literature review (SLR) of the applications, opportunities, and challenges of using drones for RIM. The SLR technique yielded 47 articles filtered from 7,900 publications from 2014 to 2022. The SLR found that key motivations for using drones in RIM are to reduce costs, improve safety, save time, improve mobility, increase flexibility, and enhance reliability. Nearly all the applications fit into the categories of defect identification, situation assessment, rail network mapping, infrastructure asset monitoring, track condition monitoring, and obstruction detection. The authors assessed the open technical, safety, and regulatory challenges. The authors also contributed a cost analysis framework, identified factors that affect drone performance in RIM, and offered implications for new theories, management, and impacts to society.

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Section: Defect Identificationmentioning

confidence: 99%

Systematic Literature Review of Drone Utility in Railway Condition Monitoring

Askarzadeh

Bridgelall

Tolliver

2023

J. Transp. Eng., Part A: Systems

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“…A dense point cloud enriched with high-resolution photographs can be the basis for assessing the technical conditions of road surfaces [38,39] and bridge structures [40]. Based on spatial data, it is possible to assess the technical condition of a railway traction network [41], determine the geometry of tracks and their connections [42], and analyze building gauges and geometric relations among objects. A dense cloud of points enriched with high-resolution photographs is the basis of the process of verifying and defining large lengths of railway lines.…”

Section: Introductionmentioning

confidence: 99%

Analysis of the Functionality of a Mobile Network of Sensors in a Construction Project Supervision System Based on Unmanned Aerial Vehicles

Strach,

Różanowski,

Pietrucha

et al. 2023

Sustainability

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This manuscript presents the results of a project related to the construction and testing of selected devices included in a space inspection and worker supervision system. The most important components of this system are a swarm of unmanned aerial vehicles, a docking station for the automatic charging of many drones, monitoring sensors, and user software that integrates all components responsible for mission planning (UAV raids) and measurement data processing. All components were built according to an original solution. The main part of this manuscript is a description of tests used to verify the functionality of a sensor network for monitoring infrastructural elements and moving objects, including people working on a construction project. As part of this research, procedures for testing sensor networks under laboratory and field conditions were developed. The tests performed demonstrated the ability of the MESH network to self-organize depending on the location of the elements in the network. The system that was built ensured the transmission of data from telemetric devices during UAV flights, regardless of the coverage of terrain by other networks, such as Wi-Fi and GSM networks. Data were sent to the end user via a LAN network based on the IP protocol. The maximum range between devices forming the network and the range limitations caused by various terrain obstacles were also determined.

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“…The work in [19] stated that the breast cancer detection system will be more effective by selecting the region of interest in the image. Intensity profile had been used for several detection purposes such as crack detection [19] and early breast cancer detection [20]. In [19], the intensity profile was used to manually indicate/detect any cancerous symptom in breast tissue.…”

mentioning

confidence: 99%

“…Intensity profile had been used for several detection purposes such as crack detection [19] and early breast cancer detection [20]. In [19], the intensity profile was used to manually indicate/detect any cancerous symptom in breast tissue. In this research, we performed a simple ROI detection for removing unexpected part or region from the thermal images using the red intensity profile.…”

mentioning

confidence: 99%

See 1 more Smart Citation

On Reducing ShuffleNets’ Block for Mobile-based Breast Cancer Detection Using Thermogram: Performance Evaluation

Ramadhana

Saddami²,

Munadi³

et al. 2022

IJEEI

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In this paper, we proposed a reduced-block-Shufflenet (RB-ShuffleNet) for thermal breast cancer detection. RB-ShuffleNet is a modification of Shufflenet obtained by reducing blocks from the original architecture. The images for training and testing were obtained from Database for Mastology Research (DMR). First, we detected and cropped the image based on the region of interest (ROI), in which the ROI is determined by using the red intensity profile. Then, the ROI images were trained using RB-ShuffleNets. In the experiments, we built eight architectures, based on ShuffleNet, each with a different number of reduced blocks. The result showed that RB-Shufflenet with four reduced blocks had fewer than 50% of the learning parameters of the original Shufflenet, without compromising its performance. The RB-ShuffleNet with up to four reduced blocks could achieve 100% testing accuracy. Furthermore, The RB-ShuffleNets performed better than MobileNetV2 and resulted in higher accuracy when fed with ROI images. Due to its light structure and good performance, we recommend RB-ShuffleNet as mobile-based CNN model which is preferable to implement in breast cancer detection.

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Localization of an unmanned aerial vehicle for crack detection in railway tracks

Cited by 9 publications

References 6 publications

Systematic Literature Review of Drone Utility in Railway Condition Monitoring

Systematic Literature Review of Drone Utility in Railway Condition Monitoring

Analysis of the Functionality of a Mobile Network of Sensors in a Construction Project Supervision System Based on Unmanned Aerial Vehicles

On Reducing ShuffleNets’ Block for Mobile-based Breast Cancer Detection Using Thermogram: Performance Evaluation

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