A Method for Detecting Damage of Traffic Marks by Half Celestial Camera Attached to Cars

Kawasaki, Takafumi; Iwamoto, Takeshi; Matsumoto, Michito; Yonezawa, Takeshi; Nakazawa, Jin; Takashio, Kazunori; Tokuda, Hideyuki

doi:10.4108/eai.22-7-2015.2260306

Cited by 5 publications

(2 citation statements)

References 5 publications

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“…The overall model performance is obtained via averaging predictive outcomes of the 5 data folds. This process has been proved to be a robust method for model hyperparameter selection [49]. Notably, in each generation, based on the computed cost function, the location of population members is updated and the stopping criterion is checked to verify whether the current generation number exceeds the allowable value.…”

Section: Methodsmentioning

confidence: 99%

Image Processing-Based Detection of Pipe Corrosion Using Texture Analysis and Metaheuristic-Optimized Machine Learning Approach

Hoang

Tran

2019

Computational Intelligence and Neuroscience

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To maintain the serviceability of buildings, the owners need to be informed about the current condition of the water supply and waste disposal systems. Therefore, timely and accurate detection of corrosion on pipe surface is a crucial task. The conventional manual surveying process performed by human inspectors is notoriously time consuming and labor intensive. Hence, this study proposes an image processing-based method for automating the task of pipe corrosion detection. Image texture including statistical measurement of image colors, gray-level co-occurrence matrix, and gray-level run length is employed to extract features of pipe surface. Support vector machine optimized by differential flower pollination is then used to construct a decision boundary that can recognize corroded and intact pipe surfaces. A dataset consisting of 2000 image samples has been collected and utilized to train and test the proposed hybrid model. Experimental results supported by the Wilcoxon signed-rank test confirm that the proposed method is highly suitable for the task of interest with an accuracy rate of 92.81%. Thus, the model proposed in this study can be a promising tool to assist building maintenance agents during the phase of pipe system survey.

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Section: Methodsmentioning

confidence: 99%

Image Processing-Based Detection of Pipe Corrosion Using Texture Analysis and Metaheuristic-Optimized Machine Learning Approach

Hoang

Tran

2019

Computational Intelligence and Neuroscience

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“…In [52], the authors presented an algorithm that identified damage to road markings. To ensure privacy, the algorithm was implemented using a half-celestial camera positioned on the underside of a vehicle, specifically a rubbish lorry.…”

Section: Related Workmentioning

confidence: 99%

Detecting Wear and Tear in Pedestrian Crossings Using Computer Vision Techniques: Approaches, Challenges, and Opportunities

Rosa,

Afonso,

Gaspar

et al. 2024

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Pedestrian crossings are an essential part of the urban landscape, providing safe passage for pedestrians to cross busy streets. While some are regulated by timed signals and are marked with signs and lights, others are simply marked on the road and do not have additional infrastructure. Nevertheless, the markings undergo wear and tear due to traffic, weather, and road maintenance activities. If pedestrian crossing markings are excessively worn, drivers may not be able to see them, which creates road safety issues. This paper presents a study of computer vision techniques that can be used to identify and classify pedestrian crossings. It first introduces the related concepts. Then, it surveys related work and categorizes existing solutions, highlighting their key features, strengths, and limitations. The most promising techniques are identified and described: Convolutional Neural Networks, Histogram of Oriented Gradients, Maximally Stable Extremal Regions, Canny Edge, and thresholding methods. Their performance is evaluated and compared on a custom dataset developed for this work. Insights on open issues and research opportunities in the field are also provided. It is shown that managers responsible for road safety, in the context of a smart city, can benefit from computer vision approaches to automate the process of determining the wear and tear of pedestrian crossings.

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Planning Placement of Distributed Sensor Nodes to Achieve Efficient Measurement

Nakamura

Ito

Sezaki

2018

Distributed, Ambient and Pervasive Interactions: Understanding Humans

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A Method for Detecting Damage of Traffic Marks by Half Celestial Camera Attached to Cars

Cited by 5 publications

References 5 publications

Image Processing-Based Detection of Pipe Corrosion Using Texture Analysis and Metaheuristic-Optimized Machine Learning Approach

Image Processing-Based Detection of Pipe Corrosion Using Texture Analysis and Metaheuristic-Optimized Machine Learning Approach

Detecting Wear and Tear in Pedestrian Crossings Using Computer Vision Techniques: Approaches, Challenges, and Opportunities

Planning Placement of Distributed Sensor Nodes to Achieve Efficient Measurement

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