Road crack detection using a single stage detector based deep neural network

Carr, Thomas; Jenkins, M.; Iglesias, Maria Insa; Buggy, Tom; Morison, Gordon

doi:10.1109/eesms.2018.8405819

Cited by 25 publications

(17 citation statements)

References 22 publications

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“…To acquire pre-trained (IMAGENET) features, we utilize pretrained models, namely VGG16, Inception-v3, and ResNet50. The choice of these models is motivated by the fact that they are widely used for damage detection tasks, as shown in the literature [6,28,34,68,77]. The traditional strategies for leveraging transfer learning include: (i) using a pre-trained model as a fixed-feature extractor (FE) in which pre-trained layers are kept frozen, (ii) finetuning (FT) all or a few of the layers of an existing model so that the weights are updated for the target task, and (iii) training IMAGENET weights from scratch and then finetuning on the target task.…”

Section: Details Of the Experimentsmentioning

confidence: 99%

Damage detection using in-domain and cross-domain transfer learning

Bukhsh

Jansen

Saeed

2021

Neural Comput & Applic

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We investigate the capabilities of transfer learning in the area of structural health monitoring. In particular, we are interested in damage detection for concrete structures. Typical image datasets for such problems are relatively small, calling for the transfer of learned representation from a related large-scale dataset. Past efforts of damage detection using images have mainly considered cross-domain transfer learning approaches using pre-trained ImageNet models that are subsequently fine-tuned for the target task. However, there are rising concerns about the generalizability of ImageNet representations for specific target domains, such as for visual inspection and medical imaging. We, therefore, evaluate a combination of in-domain and cross-domain transfer learning strategies for damage detection in bridges. We perform comprehensive comparisons to study the impact of cross-domain and in-domain transfer, with various initialization strategies, using six publicly available visual inspection datasets. The pre-trained models are also evaluated for their ability to cope with the extremely low-data regime. We show that the combination of cross-domain and in-domain transfer persistently shows superior performance specially with tiny datasets. Likewise, we also provide visual explanations of predictive models to enable algorithmic transparency and provide insights to experts about the intrinsic decision logic of typically black-box deep models.

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Section: Details Of the Experimentsmentioning

confidence: 99%

Damage detection using in-domain and cross-domain transfer learning

Bukhsh

Jansen

Saeed

2021

Neural Comput & Applic

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“…In another study, Nhat-Duc et al [26] have proposed a hybrid CNN model based on the use of metaheuristic techniques for training the DL algorithm and application in crack recognition in the pavement surface. Obviously, DL-based techniques exhibit a significant ability to detect concrete crack damage robustly and reliably [27,28]. Besides, a pretrained image-based recognition DL model could assist in the development of an automatic damage inspector, facilitating the detection of damage.…”

Section: Introductionmentioning

confidence: 99%

Development of Deep Learning Model for the Recognition of Cracks on Concrete Surfaces

Nguyen

2021

Applied Computational Intelligence and Soft Computing

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This paper is devoted to the development of a deep learning- (DL-) based model to detect crack fractures on concrete surfaces. The developed model for the classification of images was based on a DL Convolutional Neural Network (CNN). To train and validate the CNN model, a database containing 40,000 images of concrete surfaces (with and without cracks) was collected from the available literature. Several conditions on the concrete surfaces were taken into account such as illumination and surface finish (i.e., exposed, plastering, and paint). Various error measurement criteria such as accuracy, precision, recall, specificity, and F1-score were employed for accessing the quality of the developed model. Results showed that for the training dataset (50% of the database), the precision, recall, specificity, F1-score, and accuracy were 99.5%, 99.8%, 99.5%, 99.7%, and 99.7%, respectively. On the other hand, for the validating dataset, the precision, recall, specificity, F1-score, and accuracy are 96.5%, 98.8%, 96.6%, 97.7%, and 97.7%, respectively. Thus, the developed CNN model may be considered valid because it performs the classification of cracks well using the testing data. It is also confirmed that the developed DL-based model was robust and efficient, as it can take into account different conditions on the concrete surfaces. The CNN model developed in this study was compared with other works in the literature, showing that the CNN model could improve the accuracy of image classification, in comparison with previously published results. Finally, in further work, such model could be combined with Unmanned Aerial Vehicles (UAVs) to increase the productivity of concrete infrastructure inspection.

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“…After that, the road crack surface condition detection was executed by utilizing the three-dimensional reconstruction method in Fan and Dahnoun (2018). In Carr et al (2018), the road and pavement surface cracks detection was researched by utilizing the residual neural network. In addition, based on the novel data fusion scheme, the cracks detection from inspection videos of nuclear power plants has been studied by using convolutional neural network in Chen and Jahanshahi (2018).…”

Section: Introductionmentioning

confidence: 99%

Road surface real-time detection based on Raspberry Pi and recurrent neural networks

Wang

Meng

Shang

et al. 2021

Transactions of the Institute of Measurement and Control

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This paper focuses on road surface real-time detection by using tripod dolly equipped with Raspberry Pi 3 B+, MPU 9250, which is convenient to collect road surface data and realize real-time road surface detection. Firstly, six kinds of road surfaces data are collected by utilizing Raspberry Pi 3 B+ and MPU 9250. Secondly, the classifiers can be obtained by adopting several machine learning algorithms, recurrent neural networks (RNN) and long short-term memory (LSTM) neural networks. Among the machine learning classifiers, gradient boosting decision tree has the highest accuracy rate of 97.92%, which improves by 29.52% compared with KNN with the lowest accuracy rate of 75.60%. The accuracy rate of LSTM neural networks is 95.31%, which improves by 2.79% compared with RNN with the accuracy rate of 92.52%. Finally, the classifiers are embedded into the Raspberry Pi to detect the road surface in real time, and the detection time is about one second. This road surface detection system could be used in wheeled robot-car and guiding the robot-car to move smoothly.

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Road crack detection using a single stage detector based deep neural network

Cited by 25 publications

References 22 publications

Damage detection using in-domain and cross-domain transfer learning

Damage detection using in-domain and cross-domain transfer learning

Development of Deep Learning Model for the Recognition of Cracks on Concrete Surfaces

Road surface real-time detection based on Raspberry Pi and recurrent neural networks

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