Pavement crack detection and recognition using the architecture of segNet

Chen, Tingyang; Cai, Zhenhua; Zhao, Xi-Le; Chen, Chen; Liang, Xufeng; Zou, Tierui; Wang, Pan

doi:10.1016/j.jii.2020.100144

Cited by 78 publications

(36 citation statements)

References 6 publications

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“…Since this analysis is usually carried out in a two-fold action, starting with the identification of surface distresses followed by the determination of quality indexes, the functional category was sub-divided into a second level. This sub-division aimed to distinguish the data analysis methods that focused on the identification and classification of surface distresses, such as superficial cracks [28][29][30]33,40,[62][63][64][65][66][67], potholes [25,32,36], patches [18], and others [37,41], and the estimation of pavement quality indexes, such as IRI [17,19], PCI [16] and other indexes proposed by some researchers [20,22,23,27,68]. These sub-categories were, in turn, divided into the type of adopted approach, either image processing or data-driven models for the case of identification and classification of surface distresses, and model-driven or data-driven for the estimation of pavement quality indexes.…”

Section: General Aspectsmentioning

confidence: 99%

“…Image processing methods and techniques are mostly used for (but not limited to) the detection of surface distresses, such as cracks and rutting [28][29][30]33,37,41,[62][63][64][66][67][68], potholes [25,32,36,37,41,68] and patches [18,37]. As mentioned in the previous section, image-based collection methods, which usually resort to high resolution cameras, have been widely studied.…”

Section: Identification and Classification Of Surface Distressesmentioning

confidence: 99%

“…As mentioned in the previous section, image-based collection methods, which usually resort to high resolution cameras, have been widely studied. The images captured by those cameras can be processed as twodimensional (2D) images for distress detection [18,25,30,32,33,36,37,41,[62][63][64][65][66]68], or used to generate 3D models of the pavement, aiming to assess the surface roughness [61], deformation levels [40] or patch depth [32]. A common step among image processing methods is image segmentation, which involves dividing the image in smaller sections based on certain features and characteristics, culminating in the isolation of the pavement distress that is intended to be detected.…”

Section: Identification and Classification Of Surface Distressesmentioning

confidence: 99%

See 2 more Smart Citations

Integration of Smart Pavement Data with Decision Support Systems: A Systematic Review

Amândio¹,

Parente²,

Neves

et al. 2021

Buildings

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Nowadays, pavement management systems (PMS) are mainly based on monitoring processes that have been established for a long time, and strongly depend on acquired experience. However, with the emergence of smart technologies, such as internet of things and artificial intelligence, PMS could be improved by applying these new smart technologies to their decision support systems, not just by updating their data collection methodologies, but also their data analysis tools. The application of these smart technologies to the field of pavement monitoring and condition evaluation will undoubtedly contribute to more efficient, less costly, safer, and environmentally friendly methodologies. Thus, the main drive of the present work is to provide insight for the development of future decision support systems for smart pavement management by conducting a systematic literature review of the developed works that apply smart technologies to this field. The conclusions drawn from the analysis allowed for the identification of a series of future direction recommendations for researchers. In fact, future PMS should tend to be capable of collecting and analyzing data at different levels, both externally at the surface or inside the pavement, as well as to detect and predict all types of functional and structural flaws and defects.

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Section: General Aspectsmentioning

confidence: 99%

Section: Identification and Classification Of Surface Distressesmentioning

confidence: 99%

Section: Identification and Classification Of Surface Distressesmentioning

confidence: 99%

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Integration of Smart Pavement Data with Decision Support Systems: A Systematic Review

Amândio¹,

Parente²,

Neves

et al. 2021

Buildings

View full text Add to dashboard Cite

show abstract

“…The trained model obtained was having an accuracy of

defect detection. In [ 28 ] Ting yang et al proposed modified SegNet based scalable crack detection model for inspecting concrete and asphalt pavement and bridge deck cracks. The CNN network was built with VGG16 net without the top layer, initialized with open-source pre-trained weights, trained with 2000 high-resolution crack images, and achieve

defect detection accuracy.…”

Section: Introductionmentioning

confidence: 99%

Deep Learning Based Pavement Inspection Using Self-Reconfigurable Robot

Ramalingam

Hayat

Elara

et al. 2021

Sensors

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The pavement inspection task, which mainly includes crack and garbage detection, is essential and carried out frequently. The human-based or dedicated system approach for inspection can be easily carried out by integrating with the pavement sweeping machines. This work proposes a deep learning-based pavement inspection framework for self-reconfigurable robot named Panthera. Semantic segmentation framework SegNet was adopted to segment the pavement region from other objects. Deep Convolutional Neural Network (DCNN) based object detection is used to detect and localize pavement defects and garbage. Furthermore, Mobile Mapping System (MMS) was adopted for the geotagging of the defects. The proposed system was implemented and tested with the Panthera robot having NVIDIA GPU cards. The experimental results showed that the proposed technique identifies the pavement defects and litters or garbage detection with high accuracy. The experimental results on the crack and garbage detection are presented. It is found that the proposed technique is suitable for deployment in real-time for garbage detection and, eventually, sweeping or cleaning tasks.

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“…Li et al [3] enhanced datasets with deep convolutional generative adversarial network (GAN), and presented an algorithm that effectively detects complex road scenes. Referring to SegNet, Chen et al [4] proposed an encoderdecoder structural model with a fully CNN, namely, PCSN, and verified its feasibility in crack detection. Lei et al [5] developed an image recognition method for concrete crack detection, and demonstrated that the method can identify cracks in stained and moss-covered concretes.…”

Section: Introductionmentioning

confidence: 99%

Recognition and Classification of Concrete Cracks under Strong Interference Based on Convolutional Neural Network

Zhao¹,

Jiang²,

Song³

2021

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This paper proposes the UmNet model based on convolutional neutral network (CNN), aiming to improve the ability to recognize and classify concrete cracks in a background complicated by construction seams and seepage traces. The model was derived from the famous CNN AlexNet. Without changing the receptive field, large convolutional kernels were replaced with small ones to reduce the parameters, deepen the network, and increase nonlinear transforms. Next, convolutional block attention module (CBAM) was introduced to highlight the key information in images and focus on high-weight channels. Finally, Bayesian network (BN) layer and L2 regularization were added, and the number of nodes in fully connected layer were reduced. A series of comparative experiments were carried out on three datasets D, P, and W. The results show that the proposed UmNet surpassed AlexNet in the recognition accuracy on D, P, and W by 3.74%, 3.17%, and 5.74%, respectively, and reduced the number of parameters by 75.04%. Therefore, our model is an effective means to recognize and classify of concrete cracks under strong interference.

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Pavement crack detection and recognition using the architecture of segNet

Cited by 78 publications

References 6 publications

Integration of Smart Pavement Data with Decision Support Systems: A Systematic Review

Integration of Smart Pavement Data with Decision Support Systems: A Systematic Review

Deep Learning Based Pavement Inspection Using Self-Reconfigurable Robot

Recognition and Classification of Concrete Cracks under Strong Interference Based on Convolutional Neural Network

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