Flexible and stone pavements distress detection and measurement by deep learning and low-cost detection devices

Guerrieri, Marco; Parla, Giuseppe

doi:10.1016/j.engfailanal.2022.106714

Cited by 15 publications

(12 citation statements)

References 23 publications

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“…Optimised models, orthoimages, and UV maps are created for each case under study. Several works (Guerrieri and Parla, 2022;Kwon and Yu, 2019;Mishra et al, 2022;Pathak et al, 2021) have been seen using deep learning object detection to detect pathological issues and materials of the surfaces. The applications of those works are promising, but they deal with very specific objects and favourable conditions like high image quality and easily defined categories to be detected.…”

Section: Some Related Researchmentioning

confidence: 99%

Classification and Object Detection for Architectural Pathology: Practical Tests With Training Set

Zhang,

Mea,

Fiorillo

et al. 2024

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

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Abstract. Image classification and object detection techniques have been widely discussed and developed in recent years; they are the basis of various prosperous applications, for example, real-time mapping. Promising as it is, the practical test in the cultural heritage field encountered multiple problems. In this paper, the authors attempt to share the research experimentations and the empirical knowledge focusing on the classification and detection of architectural pathology. The tests are built on elaborated training sets annotated with analysed and in-advance defined categories. The trained models were examined from the perspective of evaluation sets, model explanation and unseen datasets. The outcomes indicated the mistakes and confusions behind things and stuff in the object detection efforts, to which cultural heritage and architectural field are closely related. The model also reveals specific visual patterns for recognition from thousands of instances in the training set. By digging into different aspects of model performance, the potential and limitations of these techniques in practical applications can be better understood.

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Section: Some Related Researchmentioning

confidence: 99%

Classification and Object Detection for Architectural Pathology: Practical Tests With Training Set

Zhang,

Mea,

Fiorillo

et al. 2024

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

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“…Figures 5 and 6 show the YOLOv3 architecture. For more details about this algorithm, the interested reader may consult [22,23]. Figures 5 and 6 show the YOLOv3 architecture.…”

Section: Algorithms For Crack Detectionmentioning

confidence: 99%

“…Figures 5 and 6 show the YOLOv3 architecture. For more details about this algorithm, the interested reader may consult [22,23]. The image of interest is partitioned into S × S grids; each grid determines whether the centre of the focused object is located within it.…”

Section: Algorithms For Crack Detectionmentioning

confidence: 99%

“…Nowadays, a lot of data sources for flexible pavement distress are openly available to be used, and the most popular among them are [23,[31][32][33][34][35] (Table 3): CFD dataset, AigleRN dataset, CRACK500 dataset, GAPs dataset, CrackTree200 dataset, Road Damage dataset 2018, and Road Damage dataset 2019. The model was trained on the Road Damage dataset.…”

Section: Survey Equipmentmentioning

confidence: 99%

“…The accuracy of crack detection was ensured by a strong camera calibration performed as shown in Figure 10. More details and an example of this process can be found in [23].…”

Section: Survey Vehiclementioning

confidence: 99%

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Asphalt Pavement Damage Detection through Deep Learning Technique and Cost-Effective Equipment: A Case Study in Urban Roads Crossed by Tramway Lines

Guerrieri,

Parla,

Khanmohamadi

et al. 2024

Infrastructures

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Asphalt pavements are subject to regular inspection and maintenance activities over time. Many techniques have been suggested to evaluate pavement surface conditions, but most of these are either labour-intensive tasks or require costly instruments. This article describes a robust intelligent pavement distress inspection system that uses cost-effective equipment and the ‘you only look once’ detection algorithm (YOLOv3). A dataset for flexible pavement distress detection with around 13,135 images and 30,989 bounding boxes of damage was used during the neural network training, calibration, and validation phases. During the testing phase, the model achieved a mean average precision of up to 80%, depending on the type of pavement distress. The performance metrics (loss, precision, recall, and RMSE) that were applied to estimate the object detection accuracy demonstrate that the technique can distinguish between different types of asphalt pavement damage with remarkable accuracy and precision. Moreover, the confusion matrix obtained in the validation process shows a distress classification sensitivity of up to 98.7%. The suggested technique was successfully implemented in an inspection car. Measurements conducted on urban roads crossed by tramway lines in the city of Palermo proved the real-time ability and great efficacy of the detection system, with potentially remarkable advances in asphalt pavement examination efficacy due to the high rates of correct distress detection.

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Pavement Distress Detection Using Deep Learning Based Methods: A Survey on Role, Challenges and Opportunities

Khatri¹,

Khatri²,

Kumar³

et al. 2022

Communications in Computer and Information Science

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Flexible and stone pavements distress detection and measurement by deep learning and low-cost detection devices

Cited by 15 publications

References 23 publications

Classification and Object Detection for Architectural Pathology: Practical Tests With Training Set

Classification and Object Detection for Architectural Pathology: Practical Tests With Training Set

Asphalt Pavement Damage Detection through Deep Learning Technique and Cost-Effective Equipment: A Case Study in Urban Roads Crossed by Tramway Lines

Pavement Distress Detection Using Deep Learning Based Methods: A Survey on Role, Challenges and Opportunities

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