Two-Stage Automated Defect Recognition Algorithm for the Analysis of Infrared Images

Vandone, Ambra; Rizzo, Piervincenzo; Vanali, Marcello

doi:10.1080/09349847.2011.630557

Cited by 10 publications

(6 citation statements)

References 14 publications

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“…Thermal methods employ the change in the thermal properties of the rail to detect the presence of a defect. A single frame or a video of the surface temperature distribution of the test sample is used to determine the presence of any defects [16].…”

Section: Analysis Of the State Of The Problem Under Studymentioning

confidence: 99%

The Detection of Railheads: An Innovative Direct Image Processing Method

Tverdomed,

Dmytro,

Kokriatska

et al. 2024

Sustainability

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This study presents a fully automated railhead detection method based on a direct image processing algorithm for use on a railway track. This method functions at a much faster pace than artificial intelligence algorithms that process rail images on embedded systems or low-power devices, as it does not require the use of significant computing resources. With the use of this method, railheads can be analyzed to identify the presence of cracks and other defects. We converted color images to halftone images, performed histogram equalizations to improve the contrast, applied a Gaussian filter to reduce the presence of noise, utilized convolutional filters to extract any vertical and horizontal lines, applied the Canny method and Sobel filters to refine the boundaries of the extracted lines, applied the Hough transform technique to extract lines belonging to the railhead images, and identified the segments with the highest brightness values to process the images of the railheads under study. The method of railhead separation described in this article will allow for further comprehensive diagnostics of the condition of rail threads to ensure the safe and sustainable operation of railway transport. The implementation of intelligent maintenance systems and effective monitoring of railway track conditions can reduce the negative impact on the environment and contribute to the advancement of rail transport as a sustainable, safe, and more environmentally friendly mode of transportation.

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Section: Analysis Of the State Of The Problem Under Studymentioning

confidence: 99%

The Detection of Railheads: An Innovative Direct Image Processing Method

Tverdomed,

Dmytro,

Kokriatska

et al. 2024

Sustainability

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“…These techniques exploit the change in the thermal properties of rail material in the presence of a defect. The presence of an anomaly is inferred from a single frame or from a video sequence of the temperature distribution of the surface of the considered specimen [5,34]. Some recent approaches are illustrated in Refs.…”

Section: Thermal Techniquesmentioning

confidence: 99%

Rail Diagnostics Based on Ultrasonic Guided Waves: An Overview

Bombarda

Vitetta

Ferrante³

2021

Applied Sciences

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Rail tracks undergo massive stresses that can affect their structural integrity and produce rail breakage. The last phenomenon represents a serious concern for railway management authorities, since it may cause derailments and, consequently, losses of rolling stock material and lives. Therefore, the activities of track maintenance and inspection are of paramount importance. In recent years, the use of various technologies for monitoring rails and the detection of their defects has been investigated; however, despite the important progresses in this field, substantial research efforts are still required to achieve higher scanning speeds and improve the reliability of diagnostic procedures. It is expected that, in the near future, an important role in track maintenance and inspection will be played by the ultrasonic guided wave technology. In this manuscript, its use in rail track monitoring is investigated in detail; moreover, both of the main strategies investigated in the technical literature are taken into consideration. The first strategy consists of the installation of the monitoring instrumentation on board a moving test vehicle that scans the track below while running. The second strategy, instead, is based on distributing the instrumentation throughout the entire rail network, so that continuous monitoring in quasi-real-time can be obtained. In our analysis of the proposed solutions, the prototypes and the employed methods are described.

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“…Shen et al note that the spatial resolution, probing depth and theoretical models of these NDE methods demands further study [10]. Other methods include fluorescence microscopy, used to detect image nanostructure defects in the bulk dielectric materials [11] and photothermal reflectance microscopy, a technique that employs a CCD-based thermo-reflectance microscope, which measures temperature-dependent optical reflectivity changes of materials for detecting the local nano-scale defects inside optical films [12]. Although these tools are capable of detecting different kinds of defects in optical material, they do not address thermo-mechanical response issues relevant to the laser damage.…”

Section: Introductionmentioning

confidence: 98%

Detection of pinhole defects in optical film using thermography and artificial neural network

2015

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Optical film provides anti-glare, anti-reflective, and protective features for cell phones and electronic displays such as LCD screens. Due to increased use of optical film, it is challenging for manufacturers to increase their efficiency in producing better quality films. Even a micro scratch in a high-end application of film can lead to a total failure of the display. Optical and visual methods are typically employed to detect defects, but these methods have limitations such as viewing angle artifacts and design of proper illumination source. This paper describes research to utilize artificial neural networks as a non-destructive defect detection model for predicting the presence of pinhole defects in film. An infrared camera captured the thermal response of optical film subjected to heating and cooling. Pinhole defects of various sizes (0.03mm, 0.08mm, 0.2mm, 0.4mm, 0.7mm, 1mm, 2mm, 3mm, 4mm) were investigated. Pinhole defects are one of the most common types of optical film defects. For the process of identification, thermal differences of defective and defectfree regions were investigated. An Artificial Neural Network was trained to use average absolute temperature difference and cooling rate to predict the presence of a defect. The ANN model was trained and verified using separate data sets. The ANN model was able to classify defective and non-defective samples with a 77.8% accuracy rate. The regression coefficient was 0.5874. These results suggest that artificial neural networks can be used for detecting pinhole defects.

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Two-Stage Automated Defect Recognition Algorithm for the Analysis of Infrared Images

Cited by 10 publications

References 14 publications

The Detection of Railheads: An Innovative Direct Image Processing Method

The Detection of Railheads: An Innovative Direct Image Processing Method

Rail Diagnostics Based on Ultrasonic Guided Waves: An Overview

Detection of pinhole defects in optical film using thermography and artificial neural network

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