Road crack detection using adaptive multi resolution thresholding techniques

Othman, Zuraini; Abdullah, Azizi; Kasmin, Fauziah; Ahmad, Sharifah Sakinah Syed

doi:10.12928/telkomnika.v17i4.12755

Cited by 10 publications

(6 citation statements)

References 19 publications

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“…Several studies have been conducted on AVI employing traditional image processing, specialized processing techniques as well as artificial intelligent techniques [51]. Prior defect identification, traditional image processing techniques such as edge detection and image segmentation are often utilized for the detection of defective patterns that are consistent and distinguishable from the background [52]- [56]. Furthermore, the adoption of blob detection algorithms for defects on tile Int J Elec & Comp Eng ISSN: 2088-8708  surfaces [57] and the feature-based histogram technique for the detection of defects in a textured surface [58] are examples of specialized processing techniques for surface defect detection.…”

Section: Approaches For the Identification Of Timber Defectsmentioning

confidence: 99%

A review of the automated timber defect identification approach

Chun

Hashim²,

Ahmad³

et al. 2023

IJECE

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Timber quality control is undoubtedly a very laborious process in the secondary wood industry. Manual inspections by operators are prone to human error, thereby resulting in poor timber quality inspections and low production volumes. The automation of this process using an automated vision inspection (AVI) system integrated with artificial intelligence appears to be the most plausible approach due to its ease of use and minimal operating costs. This paper provides an overview of previous works on the automated inspection of timber surface defects as well as various machine learning and deep learning approaches that have been implemented for the identification of timber defects. Contemporary algorithms and techniques used in both machine learning and deep learning are discussed and outlined in this review paper. Furthermore, the paper also highlighted the possible limitation of employing both approaches in the identification of the timber defect along with several future directions that may be further explored.

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Section: Approaches For the Identification Of Timber Defectsmentioning

confidence: 99%

A review of the automated timber defect identification approach

Chun

Hashim²,

Ahmad³

et al. 2023

IJECE

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“…Table 3 summarizes the current publically available datasets for distress detection and pavement condition assessments. The datasets are used as benchmarks to verify the crack segmentation algorithms include CrackTree200 [72], Crack500 [73], CrackForest [74], and Agile-RN [70]. Though several researchers have used it for verification of their deep learning-based architectures; however, they are limited in terms of covering various shapes, sizes, and textures of cracks formed due to different environmental conditions.…”

Section: Publicly Available Datasetsmentioning

confidence: 99%

An Exploration of Recent Intelligent Image Analysis Techniques for Visual Pavement Surface Condition Assessment

Qureshi

Hassan

McKeever

et al. 2022

Sensors

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Road pavement condition assessment is essential for maintenance, asset management, and budgeting for pavement infrastructure. Countries allocate a substantial annual budget to maintain and improve local, regional, and national highways. Pavement condition is assessed by measuring several pavement characteristics such as roughness, surface skid resistance, pavement strength, deflection, and visual surface distresses. Visual inspection identifies and quantifies surface distresses, and the condition is assessed using standard rating scales. This paper critically analyzes the research trends in the academic literature, professional practices and current commercial solutions for surface condition ratings by civil authorities. We observe that various surface condition rating systems exist, and each uses its own defined subset of pavement characteristics to evaluate pavement conditions. It is noted that automated visual sensing systems using intelligent algorithms can help reduce the cost and time required for assessing the condition of pavement infrastructure, especially for local and regional road networks. However, environmental factors, pavement types, and image collection devices are significant in this domain and lead to challenging variations. Commercial solutions for automatic pavement assessment with certain limitations exist. The topic is also a focus of academic research. More recently, academic research has pivoted toward deep learning, given that image data is now available in some form. However, research to automate pavement distress assessment often focuses on the regional pavement condition assessment standard that a country or state follows. We observe that the criteria a region adopts to make the evaluation depends on factors such as pavement construction type, type of road network in the area, flow and traffic, environmental conditions, and region’s economic situation. We summarized a list of publicly available datasets for distress detection and pavement condition assessment. We listed approaches focusing on crack segmentation and methods concentrating on distress detection and identification using object detection and classification. We segregated the recent academic literature in terms of the camera’s view and the dataset used, the year and country in which the work was published, the F1 score, and the architecture type. It is observed that the literature tends to focus more on distress identification (“presence/absence” detection) but less on distress quantification, which is essential for developing approaches for automated pavement rating.

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“…In Reference [7], a crack detection system was designed by using morphological filtering and Canny edge detection. Reference [8] combined the Otsu threshold method with the Canny edge detection algorithm, and multi-resolution crack segmentation was realized through an adaptive analysis of global and local edge features. (3) The third is an algorithm based on salience.…”

Section: Introductionmentioning

confidence: 99%

Lane Crack Detection Based on Saliency

Zhang,

Fu,

et al. 2023

Remote Sensing

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Lane cracks are one of the biggest threats to pavement conditions. The automatic detection of lane cracks can not only assist the evaluation of road quality and quantity but can also be used to develop the best crack repair plan, so as to keep the road level and ensure driving safety. Although cracks can be extracted from pavement images because the gray intensity of crack pixels is lower than the background gray intensity, it is still a challenge to extract continuous and complete cracks from the three-lane images with complex texture, high noise, and uneven illumination. Different from threshold segmentation and edge detection, this study designed a crack detection algorithm with dual positioning. An image-enhancement method based on crack saliency is proposed for the first time. Based on Bayesian probability, the saliency of each pixel judged as a crack is calculated. Then, the Fréchet distance improvement triangle relationship is introduced to determine whether the key point extracted is the fracture endpoint and whether the fast-moving method should be terminated. In addition, a complete remote-sensing process was developed to calculate the length and width of cracks by inverting the squint images collected by mobile phones. A large number of images with different types, noise, illumination, and interference conditions were tested. The average crack extraction accuracy of 89.3%, recall rate of 87.1%, and F1 value of 88.2% showed that the method could detect cracks in pavement well.

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Road crack detection using adaptive multi resolution thresholding techniques

Cited by 10 publications

References 19 publications

A review of the automated timber defect identification approach

A review of the automated timber defect identification approach

An Exploration of Recent Intelligent Image Analysis Techniques for Visual Pavement Surface Condition Assessment

Lane Crack Detection Based on Saliency

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