Hierarchical Semantic Segmentation Based Approach for Road Surface Damages and Markings Detection on Paved Road

Mouzinho, Fernao Antonio Lopes Nobre; Fukai, Hidekazu

doi:10.1109/icaicta53211.2021.9640296

Cited by 4 publications

(4 citation statements)

References 14 publications

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“…Kim et al [47] proposed a novel shallow CNN-based architecture for crack defect detection on concrete surfaces called OLeNet. Mouzinho and Fukai [59] proposed a U-Net-based framework for road surface damages and markings detection on paved roads, to avoid off-road defect detection. Kumar, Sharma, et al [61] showed a semantic segmentation of concrete surface defects based on Mask R-CNN with transfer learning.…”

Section: Buildingmentioning

confidence: 99%

“…To achieve a great variety of images, the authors take advantage of different time periods (morning, noon, evening), different shooting distances, light and shadow illuminations, etc. Consequently, the authors use unmanned aerial vehicles for buildings with difficult access such as in [47,61,63,65], or they use ground vehicles for roads such as in [37,52,59]. Moreover, it is the surface where industrial cameras do not stand out; on the contrary, other types of cameras are used, such as Canon (SX60 HS) [63], the Transcend DrivePro 230 camera [59], or smartphone cameras [52], demonstrating that on this surface the important thing is to find the way to access the place to take the picture.…”

Section: Buildingmentioning

confidence: 99%

“…Consequently, the authors use unmanned aerial vehicles for buildings with difficult access such as in [47,61,63,65], or they use ground vehicles for roads such as in [37,52,59]. Moreover, it is the surface where industrial cameras do not stand out; on the contrary, other types of cameras are used, such as Canon (SX60 HS) [63], the Transcend DrivePro 230 camera [59], or smartphone cameras [52], demonstrating that on this surface the important thing is to find the way to access the place to take the picture. These created datasets are mostly kept private, except for the brick/masonry dataset [70], created and available online, along with the code to replicate the project.…”

Section: Buildingmentioning

confidence: 99%

See 2 more Smart Citations

A Systematic Review on Deep Learning with CNNs Applied to Surface Defect Detection

Cumbajin,

Rodrigues,

Costa

et al. 2023

J. Imaging

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Surface defect detection with machine learning has become an important tool in industries and a large field of study for researchers or workers in recent years. It is necessary to have a simplified source of information that helps us to better focus on one type of surface. In this systematic review, we present a classification for surface defect detection based on convolutional neural networks (CNNs) focused on surface types. Findings: Out of 253 records identified, 59 primary studies were eligible. Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, we analyzed the structures of each study and the concepts related to defects and their types on surfaces. The presented review is mainly focused on finding a classification for the types of surfaces most used in industry (metal, building, ceramic, wood, and special). We delve into the specifics of each surface category, offering illustrative examples of their applications within both industrial and laboratory settings. Furthermore, we propose a new taxonomy of machine learning based on the obtained results and collected information. We summarized the studies and extracted the main characteristics such as type of surface, problem types, timeline, type of network, techniques, and datasets. Among the most relevant results of our analysis, we found that the metallic surface is the most used, as it is the one found in 62.71% of the studies, and the most prevalent problem type is classification, accounting for 49.15% of the total. Furthermore, we observe that transfer learning was employed in 83.05% of the studies, while data augmentation was utilized in 59.32%. Our findings also provide insights into the cameras most frequently employed, along with the strategies adopted to address illumination challenges present in certain articles and the approach to creating datasets for real-world applications. The main results presented in this review allow for a quick and efficient search of information for researchers and professionals interested in improving the results of their defect detection projects. Finally, we analyzed the trends that could open new fields of study for future research in the area of surface defect detection.

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Section: Buildingmentioning

confidence: 99%

Section: Buildingmentioning

confidence: 99%

Section: Buildingmentioning

confidence: 99%

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A Systematic Review on Deep Learning with CNNs Applied to Surface Defect Detection

Cumbajin,

Rodrigues,

Costa

et al. 2023

J. Imaging

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“…Semantic segmentation has been used to solve road-related problems such as identifying road boundaries for autonomous vehicles [ 17 ], preventing pedestrian collision [ 18 ], and detecting road surface damages [ 19 ]. Our case of road dust differs from the cases above in that the boundary of the object of interest, i.e., dust cloud, may not be as clear as a rigid object.…”

Section: Introductionmentioning

confidence: 99%

Beyond Conventional Monitoring: A Semantic Segmentation Approach to Quantifying Traffic-Induced Dust on Unsealed Roads

de Silva,

Ranasinghe,

Sounthararajah

et al. 2024

Sensors

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Road dust is a mixture of fine and coarse particles released into the air due to an external force, such as tire–ground friction or wind, which is harmful to human health when inhaled. Continuous dust emission from the road surfaces is detrimental to the road itself and the road users. Due to this, multiple dust monitoring and control techniques are currently adopted in the world. The current dust monitoring methods require expensive equipment and expertise. This study introduces a novel pragmatic and robust approach to quantifying traffic-induced road dust using a deep learning method called semantic segmentation. Based on the authors’ previous works, the best-performing semantic segmentation machine learning models were selected and used to identify dust in an image pixel-wise. The total number of dust pixels was then correlated with real-world dust measurements obtained from a research-grade dust monitor. Our method shows that semantic segmentation can be adopted to quantify traffic-induced dust reasonably. Over 90% of the predictions from both correlations fall in true positive quadrant, indicating that when dust concentrations are below the threshold, the segmentation can accurately predict them. The results were validated and extended for real-time application. Our code implementation is publicly available.

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A Road Damage Segmentation Method for Complex Environment Based on Improved UNet

Liu,

Yuan,

Chen

2023

Lecture Notes in Computer Science

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Hierarchical Semantic Segmentation Based Approach for Road Surface Damages and Markings Detection on Paved Road

Cited by 4 publications

References 14 publications

A Systematic Review on Deep Learning with CNNs Applied to Surface Defect Detection

A Systematic Review on Deep Learning with CNNs Applied to Surface Defect Detection

Beyond Conventional Monitoring: A Semantic Segmentation Approach to Quantifying Traffic-Induced Dust on Unsealed Roads

A Road Damage Segmentation Method for Complex Environment Based on Improved UNet

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