An Efficient Method for Target Extraction of Infrared Images

Liu, Ying; Mao, Xingjin

doi:10.1007/978-3-642-16530-6_23

Cited by 7 publications

(4 citation statements)

References 7 publications

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“…Due to the nature of IR images which are quite different in comparison to visual light images, extracting the hot regions within an IR image is a very challenging task [34]. The distribution of pixel intensities in IR images is based on the heat distribution of an object.…”

Section: Automated Diagnostic Methodsmentioning

confidence: 99%

Design of an Infrared Image Processing Pipeline for Robotic Inspection of Conveyor Systems in Opencast Mining Sites

Siami¹,

Barszcz

Wodecki

et al. 2022

Energies

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Conveying systems play an essential role in the continuous horizontal transportation of raw materials in mining sites. Regular inspections of conveyor system structures and their components, especially idlers, are essential for proper maintenance. Traditional inspection methods are labor-intensive and hazardous; therefore, robot-based thermography can be considered a quality assessment tool for the precise detection and localization of overheated idlers in opencast mining sites. This paper proposes an infrared image processing pipeline for the automatic detection and analysis of overheated idlers. The proposed image processing pipeline can be used for the identification of significant temperature anomalies such as hotspots and hot areas in infrared images. For the identification of such defects in idlers, firstly, the histogram of captured infrared images was analyzed and improved through the pre-processing stages. Afterward, the location of thermal anomalies in infrared images was extracted. Finally, for the validation of segmentation results, the shapes and locations of segmented hot spots were compared with RGB images that were synchronized by captured infrared images. A quantitative evaluation of the proposed method for the condition monitoring of belt conveyor idlers in an open-cast mining site shows the applicability of our approach.

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Section: Automated Diagnostic Methodsmentioning

confidence: 99%

Design of an Infrared Image Processing Pipeline for Robotic Inspection of Conveyor Systems in Opencast Mining Sites

Siami¹,

Barszcz

Wodecki

et al. 2022

Energies

View full text Add to dashboard Cite

show abstract

“…The classical threshold-based segmentation techniques can accurately segment the ROI areas in images that are captured in visual light. However, due to the nature of IR images, the accuracy of final results can be affected by over-and undersegmentation [30][31][32]. Furthermore, in our case study, the presence of reflective objects with high emissivity values that are not related to idlers can create complex background conditions which directly affect the accuracy of segmentation results [33].…”

Section: Introductionmentioning

confidence: 91%

Automated Identification of Overheated Belt Conveyor Idlers in Thermal Images with Complex Backgrounds Using Binary Classification with CNN

Siami¹,

Barszcz

Wodecki

et al. 2022

Sensors

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Mechanical industrial infrastructures in mining sites must be monitored regularly. Conveyor systems are mechanical systems that are commonly used for safe and efficient transportation of bulk goods in mines. Regular inspection of conveyor systems is a challenging task for mining enterprises, as conveyor systems’ lengths can reach tens of kilometers, where several thousand idlers need to be monitored. Considering the harsh environmental conditions that can affect human health, manual inspection of conveyor systems can be extremely difficult. Hence, the authors proposed an automatic robotics-based inspection for condition monitoring of belt conveyor idlers using infrared images, instead of vibrations and acoustic signals that are commonly used for condition monitoring applications. The first step in the whole process is to segment the overheated idlers from the complex background. However, classical image segmentation techniques do not always deliver accurate results in the detection of target in infrared images with complex backgrounds. For improving the quality of captured infrared images, preprocessing stages are introduced. Afterward, an anomaly detection method based on an outlier detection technique is applied to the preprocessed image for the segmentation of hotspots. Due to the presence of different thermal sources in mining sites that can be captured and wrongly identified as overheated idlers, in this research, we address the overheated idler detection process as an image binary classification task. For this reason, a Convolutional Neural Network (CNN) was used for the binary classification of the segmented thermal images. The accuracy of the proposed condition monitoring technique was compared with our previous research. The metrics for the previous methodology reach a precision of 0.4590 and an F1 score of 0.6292. The metrics for the proposed method reach a precision of 0.9740 and an F1 score of 0.9782. The proposed classification method considerably improved our previous results in terms of the true identification of overheated idlers in the presence of complex backgrounds.

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“…It brings some difficulties to image segmentation due to its over-centralized intensity distribution and low intensity contrast [43]. Furthermore, extracting the hot regions within an infrared image is a very challenging task, especially when the image contains a very complex background and low signal-to-noise ratio (SNR) [44]. Another reason of producing the low-quality images such as blurring effect, low target-to-background contrast and noises in infrared images is due to the limitation of infrared camera technology availability [45].…”

Section: Automated Diagnostic Systemmentioning

confidence: 99%

Recent progress in diagnosing the reliability of electrical equipment by using infrared thermography

Jadin

Taib

2012

Infrared Physics & Technology

257

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An Efficient Method for Target Extraction of Infrared Images

Cited by 7 publications

References 7 publications

Design of an Infrared Image Processing Pipeline for Robotic Inspection of Conveyor Systems in Opencast Mining Sites

Design of an Infrared Image Processing Pipeline for Robotic Inspection of Conveyor Systems in Opencast Mining Sites

Automated Identification of Overheated Belt Conveyor Idlers in Thermal Images with Complex Backgrounds Using Binary Classification with CNN

Recent progress in diagnosing the reliability of electrical equipment by using infrared thermography

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