A neural network image classification system for automatic inspection

Mashford, John

doi:10.1109/icnn.1995.487504

Cited by 13 publications

(7 citation statements)

References 9 publications

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“…Artificial Neural Networks and Genetic Algorithms can be used to solve various kinds of problems varying from function approximation and clustering problems to function maximization or minimization problems. These techniques have been used in an enormous number of studies by the authors of this paper and other authors to aid in solving various problems such as image processing, finding certain patterns and networking problems, such as load balancing, finding the best route and, in the current paper, to aid in the estimation of quality in VoIP networks [1,2,3,5,7,8,21,22,25].…”

Section: The Proposed Techniquementioning

confidence: 99%

AN IMPROVED E-MODEL USING ARTIFICIAL NEURAL NETWORK VoIP QUALITY PREDICTOR

Al-Akhras

Almomani

Sleit

2011

NNW

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Nowadays, remote sensing technology is being used as an essential tool for monitoring and detecting oil spills to take precautions and to prevent the damages to the marine environment. As an important branch of remote sensing, satellite based synthetic aperture radar imagery (SAR) is the most effective way to accomplish these tasks. Since a marine surface with oil spill seems as a dark object because of much lower backscattered energy, the main problem is to recognize and differentiate the dark objects of oil spills from others to be formed by oceanographic and atmospheric conditions. In this study, Radarsat-1 images covering Lebanese coasts were employed for oil spill detection. For this purpose, a powerful classifier, Artificial Neural Network Multilayer Perceptron (ANN MLP) was used. As the original contribution of the paper, the network was trained by a novel heuristic optimization algorithm known as Artificial Bee Colony (ABC) method besides the conventional Backpropagation (BP) and Levenberg-Marquardt (LM) learning algorithms. A comparison and evaluation of different network training algorithms regarding reliability of detection and robustness show that for this problem best result is achieved with the Artificial Bee Colony algorithm (ABC).

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Section: The Proposed Techniquementioning

confidence: 99%

AN IMPROVED E-MODEL USING ARTIFICIAL NEURAL NETWORK VoIP QUALITY PREDICTOR

Al-Akhras

Almomani

Sleit

2011

NNW

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“…The PIRAT system [10] was made up of an inspection device providing range images (obtained by laser striping) together with an interpretation system using neural networks and other AI techniques [11]. The image acquisition device for the PIRAT system is shown in Figure 1.…”

Section: Image Recognition For Automatic Pipe Inspectionmentioning

confidence: 99%

An Approach to Pipe Image Interpretation Based Condition Assessment for Automatic Pipe Inspection

Mashford

Marlow

Burn

2009

Advances in Civil Engineering

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Condition assessment forms an important part of the asset management of buried pipelines. This is carried out through the use of inspection systems which usually consist of an image acquisition device attached to a mobile robotic platform. Complete or partial automation of image interpretation could increase the efficiency and objectivity of pipe inspection. A key component of an automatic pipe inspection system is the segmentation module. This paper describes an approach to automatic pipe inspection using pixel-based segmentation of colour images by support vector machine (SVM) coupled with morphological analysis of the principal component of the segmented image. The morphological analysis allows the principal component of the segmented image to be decomposed into the pipe flow lines region, the pipe joints, and adjoining defects. A simple approach to detecting pipe connections using fuzzy membership functions relating to defect size and location is also described.

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“…The classifier has four internal stages: ROI scaling, ROI patch transformation, feature vector computation, and then classification of the feature vector, using a Bayesian decision tree of feed-forward neural networks. The classifier is described fully in Mashford (1995).…”

Section: Classificationmentioning

confidence: 99%

PIRAT—A System for Quantitative Sewer Pipe Assessment

Kirkham¹,

Kearney²,

Rogers

et al. 2000

The International Journal of Robotics Research

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Sewers are aging, expensive assets that attract public attention only when they fail. Sewer operators are under increasing pressure to minimise their maintenance costs, while preventing sewer failures. Inspection can give early warning of failures and allow economical repair under noncrisis conditions. Current inspection techniques are subjective and detect only gross defects reliably. They cannot provide the data needed to confidently plan long-term maintenance. This paper describes PIRAT, a quantitative technique for sewer inspection. PIRAT measures the internal geometry of the sewer and then analyses these data to detect, classify, and rate defects automatically using artificial intelligence techniques. We describe the measuring system and present and discuss geometry results for different types of sewers. The defect analysis techniques are outlined and a sample defect report presented. PIRAT's defect reports are compared with reports from the conventional technique and the discrepancies discussed. We relate PIRAT to other work in sewer robotics.

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A neural network image classification system for automatic inspection

Cited by 13 publications

References 9 publications

AN IMPROVED E-MODEL USING ARTIFICIAL NEURAL NETWORK VoIP QUALITY PREDICTOR

AN IMPROVED E-MODEL USING ARTIFICIAL NEURAL NETWORK VoIP QUALITY PREDICTOR

An Approach to Pipe Image Interpretation Based Condition Assessment for Automatic Pipe Inspection

PIRAT—A System for Quantitative Sewer Pipe Assessment

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