2D image based road pavement crack detection by calculating minimal paths and dynamic programming

Avila, Manuel; Bégot, Stéphane; Duculty, Florent; Nguyen, Tien Sy

doi:10.1109/icip.2014.7025157

Cited by 46 publications

(26 citation statements)

References 10 publications

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“…Reconnection is a subjective process, and strategies vary significantly. Some works ignore this step altogether and rely on fine-tuning the local detection 4,22 in order to eliminate false positives. Reconnection may be performed on a binary map 2 provided by the local analysis, but in this case the process relies only on geometric considerations.…”

Section: Related Workmentioning

confidence: 99%

Robust crack detection for unmanned aerial vehicles inspection in ana-contrariodecision framework

Aldea¹,

Hégarat‐Mascle²

2015

J. Electron. Imaging

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Mascle. Robust crack detection for unmanned aerial vehicles inspection in an a-contrario decision framework.Abstract. We are interested in the performance of currently available algorithms for the detection of cracks in the specific context of aerial inspection, which is characterized by image quality degradation. We focus on two widely used families of algorithms based on minimal cost path analysis and on image percolation, and we highlight their limitations in this context. Furthermore, we propose an improved strategy based on a-contrario modeling which is able to withstand significant motion blur due to the absence of various thresholds which are usually required in order to cope with varying crack appearances and with varying levels of degradation. The experiments are performed on real image datasets to which we applied complex blur, and the results show that the proposed strategy is effective, while other methods which perform well on good quality data experience significant difficulties with degraded images.

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Section: Related Workmentioning

confidence: 99%

Robust crack detection for unmanned aerial vehicles inspection in ana-contrariodecision framework

Aldea¹,

Hégarat‐Mascle²

2015

J. Electron. Imaging

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“…German et al 2 exploited entropy-based thresholding in conjunction with template matching and morphological operations for rapid detection and quantification of concrete spalling during post earthquake safety assessments. Similar studies related to damage detection in other forms of structural systems include identification of cracks in bridges, 3 pavement surfaces, [4][5][6][7][8][9][10] and underground pipes and subway tunnels. 11,12 Several researchers [13][14][15][16] in the past also focused on machine learning-based techniques for automatic vision-based damage detection where the feature vectors are selected manually in these methods.…”

Section: Introductionmentioning

confidence: 99%

Deep learning‐based multi‐class damage detection for autonomous post‐disaster reconnaissance

Mondal

Jahanshahi

et al. 2020

Struct Control Health Monit

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Timely assessment of damages induced to buildings due to an earthquake is critical for ensuring life safety, mitigating financial losses, and expediting the rehabilitation process as well as enhancing the structural resilience where resilience is measured by an infrastructure's capacity to restore full functionality post extreme events. Since manual inspection is expensive, time consuming and risky, low‐cost unmanned aerial vehicles or robots can be leveraged as a viable alternative for quick reconnaissance. Visual data captured by the sensors mounted on the robots can be analyzed, and the damages can be detected and classified autonomously. The present study proposes the use of deep learning‐based approaches to this end. Region‐based convolutional neural network (Faster RCNN) is exploited to detect four different damage types, namely, surface crack, spalling (which includes façade spalling and concrete spalling), and severe damage with exposed rebars and severely buckled rebars. The performance of the proposed approach is evaluated on manually annotated image data collected from reinforced concrete buildings damaged under several past earthquakes such as Nepal (2015), Taiwan (2016), Ecuador (2016), Erzincan (1992), Duzce (1999), Bingol (2003), Peru (2007), Wenchuan (2008), and Haiti (2010). Several experiments are presented in the paper to illustrate the capabilities, as well as the limitations, of the proposed approach for earthquake reconnaissance. It was observed that Inception‐ResNet‐v2 significantly outperforms the other networks considered in this study. The research outcome is a stepping stone forward to facilitate the autonomous assessment of buildings where this can be potentially useful for insurance companies, government agencies, and property owners.

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“…However, these unsupervised algorithms do not perform well on crack images under different lighting conditions and with complicated backgrounds, such as oil stains and dirt. Other methods have also been used to detect structural cracks such as Minimal Path Selection (MPS) (Amhaz, Chambon, Idier, & Baltazart, 2016;Avila, Begot, Duculty, & Nguyen, 2014), Gaussian Mixture Model (GMM) (Oliveira & Correia, 2013), and 3D shadow modeling (Zhang, Wang, & Ai, 2017a). However, a challenge with using these traditional image processing techniques is the problem-dependent structural impairment characteristics which makes generalized detection of diverse crack types difficult.…”

Section: Introductionmentioning

confidence: 99%

Concrete crack detection using context‐aware deep semantic segmentation network

Zhang

Rajan

Story

2019

Computer aided Civil Eng

150

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Computer‐vision and deep‐learning techniques are being increasingly applied to inspect, monitor, and assess infrastructure conditions including detection of cracks. Traditional vision‐based methods to detect cracks lack accuracy and generalization to work on complicated infrastructural conditions. This paper presents a novel context‐aware deep convolutional semantic segmentation network to effectively detect cracks in structural infrastructure under various conditions. The proposed method applies a pixel‐wise deep semantic segmentation network to segment the cracks on images with arbitrary sizes without retraining the prediction network. Meanwhile, a context‐aware fusion algorithm that leverages local cross‐state and cross‐space constraints is proposed to fuse the predictions of image patches. This method is evaluated on three datasets: CrackForest Dataset (CFD) and Tomorrows Road Infrastructure Monitoring, Management Dataset (TRIMMD) and a Customized Field Test Dataset (CFTD) and achieves Boundary F1 (BF) score of 0.8234, 0.8252, and 0.7937 under 2‐pixel error tolerance margin in CFD, TRIMMD, and CFTD, respectively. The proposed method advances the state‐of‐the‐art performance of BF score by approximately 2.71% in CFD, 1.47% in TRIMMD, and 4.14% in CFTD. Moreover, the averaged processing time of the proposed system is 0.7 s on a typical desktop with Intel® Quad‐Core™ i7‐7700 CPU@3.6 GHz Processor, 16GB RAM and NVIDIA GeForce GTX 1060 6GB GPU for an image of size 256 × 256 pixels.

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2D image based road pavement crack detection by calculating minimal paths and dynamic programming

Cited by 46 publications

References 10 publications

Robust crack detection for unmanned aerial vehicles inspection in ana-contrariodecision framework

Robust crack detection for unmanned aerial vehicles inspection in ana-contrariodecision framework

Deep learning‐based multi‐class damage detection for autonomous post‐disaster reconnaissance

Concrete crack detection using context‐aware deep semantic segmentation network

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