Detecting Potholes Using Image Processing Techniques and Real-World Footage

Naik, Minal; Nirmalrani, V.

doi:10.1007/978-981-16-1056-1_72

Cited by 10 publications

(9 citation statements)

References 12 publications

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“…This category was not misclassified in any way (potholes were not misclassified as other categories or other categories were not misclassified as potholes). It can be inferred from these results that Nienaber et al's [8] hypotheses are accurate, as the model was able to distinguish craters from other study categories. This may be due to the difference in pothole outlines as they appear in images or the width of the contrast between the distress and the remainder of the pavement surface.…”

Section: Resultsmentioning

confidence: 53%

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Utilising Convolutional Neural Networks for Pavement Distress Classification and Detection

Sharif,

Emiola,

Zorto

et al. 2023

2023 International Conference on Innovation and Intelligence for Informatics, Computing, and Technologies (3ICT)

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This paper examines deep learning models for accurate and efficient identification and classification of pavement distresses. In it, a variety of related studies conducted on the topic as well as the various identification and classification methods proposed, such as edge detection, machine learning classification informed by statistical feature extraction, artificial neural networks, and real-time object detection systems, are discussed. The study investigates the effect of image processing techniques such as grayscaling, background subtraction, and image resizing on the performance and generalizability of the models. Using convolutional neural networks (CNN) architectures, this paper proposes a model that correctly classifies images into five pavement distress categories, namely fatigue (or alligator), longitudinal, transverse, patches, and craters, with an accuracy rate of 90.4% and a recall rate of 90.1%. The model is contrasted to a current state-of-the-art model based on the You Only Look Once framework as well as a baseline CNN model to demonstrate the impact of the image processing and architecture building techniques discussed on performance. The findings of this paper contribute to the fields of computer vision and infrastructure monitoring by demonstrating the efficacy of convolutional neural networks (CNNs) in image classification and the viability of using CNNbased models to automate pavement condition monitoring.

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Section: Resultsmentioning

confidence: 53%

“…1) Edge Detection: Nienaber et al [8] conducted a study on the use of edge detection to identify potholes in South African roads. They utilized the Canny edge detection algorithm to identify and highlight the distinctive edges of potholes in the captured images.…”

Section: B Image Classificationmentioning

confidence: 99%

Utilising Convolutional Neural Networks for Pavement Distress Classification and Detection

Sharif,

Emiola,

Zorto

et al. 2023

2023 International Conference on Innovation and Intelligence for Informatics, Computing, and Technologies (3ICT)

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“…The two classes of concrete images -con-crack and crack, are balanced in number. The total number of CCIC dataset are [41] annotated 53 images containing 97 potholes from the newly created pothole image library. The video frames were obtained while driving at 40 km/h, from which the images of resolution size 3880 × 2760 pixels are selected to involve one pothole in a single frame.…”

Section: Image-level Datasetmentioning

confidence: 99%

A review on computer vision and machine learning techniques for automated road surface defect and distress detection

Chen

Yongchareon

Knoche

2023

SCS

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As the pace grows in the development of image processing techniques and the current applications rise in machine learning and deep learning techniques for visual inspections and physical assessment, this article reviews the existing literature. It provides a detailed synthesis of the overview of surface pavement conditions, computer-vision-based technologies for road damage detection, various datasets and data collection methods. We analyse and compare different machine-learning methods and models proposed in the literature and identify challenges that need to be addressed in the future in road surface defect detection.

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“…Based on the fundamental properties of potholes, the paper by Nienaber et al [18] discusses an algorithmic approach to detecting potholes without requiring any machine learning. As the approach is visual, it is clear that the solution will depend on lighting, angle or point of view, and other factors that obstruct the view of potholes.…”

Section: Introductionmentioning

confidence: 99%

Pothole recognition using convolution neural networks and transfer learning

Senigalakuruba¹,

Pabba²

2023

IJ-AI

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Potholes have been and still are a huge problem for every walk of life. There are many deaths and accidents reported daily due to that very problem. For that reason, pothole recognition comes into the picture. To maintain and preserve a road, it is vital to detect potholes. It also helps in the prevention of accidents. Roads play an important part in day-to-day transportation for every person around the world. But the quality of roads decreases drastically due to the way of usage and aging. The existing methods take much time and manpower to repair the damaged areas. The entire process is slowing down just because an expert team is checking whether there is a pothole at the reported location or not. So, if we automate the process of detection of potholes from a set of images reported from a particular location and appropriately alerting the authorities with the amount of damage, the process speeds up exponentially. We must solve the major problem of pothole recognition by using machine learning algorithms. This paper will discuss a convolution neural network-based and a transfer learning-based solution for pothole recognition.

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Detecting Potholes Using Image Processing Techniques and Real-World Footage

Cited by 10 publications

References 12 publications

Utilising Convolutional Neural Networks for Pavement Distress Classification and Detection

Utilising Convolutional Neural Networks for Pavement Distress Classification and Detection

A review on computer vision and machine learning techniques for automated road surface defect and distress detection

Pothole recognition using convolution neural networks and transfer learning

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