Deep Learning to Detect Road Distress from Unmanned Aerial System Imagery

Truong, Long Ngo Hoang; Mora, Omar E.; Cheng, Wen; Tang, Hairui; Singh, Mankirat

doi:10.1177/03611981211004973

Cited by 11 publications

(7 citation statements)

References 33 publications

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“…Moreover, a series of random affine transformations like scaling, translation, rotation, and shearing can significantly contribute to the increment in prediction accuracy. The effectiveness of the data augmentation in reducing overfitting and increasing prediction accuracy is widely verified by experiments presented in the research of Mask R-CNN models in the field of transportation (73,(80)(81)(82)(83)(84).…”

Section: Model Trainingmentioning

confidence: 86%

Rotated Mask Region-Based Convolutional Neural Network Detection for Parking Space Management System

Truong

Clay

Mora

et al. 2022

Transportation Research Record: Journal of the Transportation R

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Parking space management systems help organize and optimize available parking spaces for consumers, making the process of finding and using parking spaces more efficient. Current parking space management systems include manual recognition, the employment of magnetic and ultrasonic sensors, and, recently, computer vision (CV). One relatively new region-based convolutional neural network (R-CNN) model, Mask R-CNN, has shown promise in its ability to detect objects and has demonstrated superior performance over many other popular CV methods. Building on Mask R-CNN, an updated version, Rotated Mask R-CNN, which can generate bounding boxes the axes of which are rotated with respect to the image’s axis, was proposed to address the limitation of Mask R-CNN. Albeit with the documented theoretical benefits, the application of the rotated version is rare because of its recent invention. To this end, the study aims to detect vehicle instances in one parking lot using various Rotated Mask R-CNN models based on unmanned aircraft system collected images. Both average precision and average recall were utilized to assess the performance of the alternative models with different backbone and head networks. The results reveal the high accuracy level associated with Rotated Mask R-CNN in real-time detection of vehicles. In addition, the results indicate that the inference speed and total loss are highly correlated with head networks and training schedules.

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Section: Model Trainingmentioning

confidence: 86%

Rotated Mask Region-Based Convolutional Neural Network Detection for Parking Space Management System

Truong

Clay

Mora

et al. 2022

Transportation Research Record: Journal of the Transportation R

Self Cite

View full text Add to dashboard Cite

show abstract

“…Crack segmentation refers to the classification of image pixels into crack and non-crack pixels. Various DL methods have been implemented for the detection and segmentation of cracks in various structures [46,48,[94][95][96][97][98][99][100][101][102][103][104][105][106][107][108][109][110][111][112][113]. A fully convolutional network (FCN) named Ci-Net trained on two publicly available datasets (CFD [114] and TITS2016 [115] was proposed in [103] for the identification and segmentation of structural cracks.…”

Section: Based Segmentation Algorithmsmentioning

confidence: 99%

Bibliometric Analysis and Review of Deep Learning-Based Crack Detection Literature Published between 2010 and 2022

et al. 2022

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The use of deep learning (DL) in civil inspection, especially in crack detection, has increased over the past years to ensure long-term structural safety and integrity. To achieve a better understanding of the research work on crack detection using DL approaches, this paper aims to provide a bibliometric analysis and review of the current literature on DL-based crack detection published between 2010 and 2022. The search from Web of Science (WoS) and Scopus, two widely accepted bibliographic databases, resulted in 165 articles published in top journals and conferences, showing the rapid increase in publications in this area since 2018. The evolution and state-of-the-art approaches to crack detection using deep learning are reviewed and analyzed based on datasets, network architecture, domain, and performance of each study. Overall, this review article stands as a reference for researchers working in the field of crack detection using deep learning techniques to achieve optimal precision and computational efficiency performance in light of electing the most effective combination of dataset characteristics and network architecture for each domain. Finally, the challenges, gaps, and future directions are provided to researchers to explore various solutions pertaining to (a) automatic recognition of crack type and severity, (b) dataset availability and suitability, (c) efficient data preprocessing techniques, (d) automatic labeling approaches for crack detection, (e) parameter tuning and optimization, (f) using 3D images and data fusion, (g) real-time crack detection, and (h) increasing segmentation accuracy at the pixel level.

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“…The second category is the object detection, which determines the road damage in very fine resolution imagery using a detection box [34], [35], [36]. It includes the one-step methods that directly detect the damage [9], [37], [38], and the two-step methods that use a detection step following classification [39], [40], [41], [42]. These methods generally used rectangular boxes to locate the detected damaged objects.…”

Section: B Deep Learningmentioning

confidence: 99%

A New Method for Automated Monitoring of Road Pavement Aging Conditions Based on Recurrent Neural Network

Chen

Zhang

et al. 2022

IEEE Trans. Intell. Transport. Syst.

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The automated monitoring of road pavement conditions is a challenging subject in intelligent transportation. However, the existing studies mostly focus on extracting pavement damages such as cracks, while the pavement aging conditions are still less investigated. In this paper, a novel method based on a modified recurrent neural network is designed for automated monitoring of asphalt pavement aging phenomena from fineresolution satellite imagery. A spectral augmentation method is proposed to enhance the spectral details of the road pavements. A novel loss function is also proposed to improve the bi-directional gated recurrent unit (Bi-GRU) network in order to better classify different degrees of road pavement aging and nonpavement objects. In order to demonstrate the outperformance of the modified network Bi-GRU+, the Worldview-2 satellite image (16360 * 7728) covering 16 asphalt roads in the southwestern suburb of Beijing City is used. The results show that the proposed approach has better performance than existing machine learning methods, with an overall accuracy of 98.16% and a Kappa coefficient of 0.97. The overall processing time of the proposed method is 7836 seconds in our case study. The proposed method is efficient for large-scale monitoring of road health conditions from fine-resolution satellite imagery. It can become a part of intelligent transportation and provide a new foundation for largerange automated monitoring of road pavement aging conditions.

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Deep Learning to Detect Road Distress from Unmanned Aerial System Imagery

Cited by 11 publications

References 33 publications

Rotated Mask Region-Based Convolutional Neural Network Detection for Parking Space Management System

Rotated Mask Region-Based Convolutional Neural Network Detection for Parking Space Management System

Bibliometric Analysis and Review of Deep Learning-Based Crack Detection Literature Published between 2010 and 2022

A New Method for Automated Monitoring of Road Pavement Aging Conditions Based on Recurrent Neural Network

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