Mr. Bruno Oliveira Santos Profile

Bruno Oliveira Santos

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Publications (2)

Proceedings Article | 9 May 2018 Presentation + Paper

Classification of biological colonization on concrete surfaces using false colour HSV images, including near-infrared information

Bruno O. Santos, Jónatas Valença, Eduardo Júlio

Proceedings Volume 10680, 1068003 (2018) https://doi.org/10.1117/12.2307728

KEYWORDS: RGB color model, Image classification, Image segmentation, Near infrared, Image processing, Hyperspectral imaging, Reliability, Image analysis

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Detection and mapping crack patterns are key issues for structural assessment of concrete structures. The use of image processing for identification of pathologies has undergone major developments, since it is a noninvasive technique providing the precision and reliability required for the task. The authors have developed a method, named SurfCrete, to materials and damages classification on concrete structures, including mapping cracks. This is based on analysis of multi-spectral images, including visible and near infra-red (NIR) regions of the electromagnetic spectrum. Latest improvements include the use of hyperspectral image analysis for crack detection, based on image clustering. The drawbacks of the developed methods are the difficulties usually shown when dealing with surfaces presenting several damages and materials besides cracks, namely due to the presence of biological colonization, repairing mortars, delamination and efflorescence, among other anomalies commonly found on concrete structures. Furthermore, when surfaces are subjected to different light conditions, this also influences the accurate classification of cracks. In this paper, an evolution of the method previously developed, herein named SurfCrete-HSV, is presented. The new method is completely focused on classifying biological colonization based on the classification of HSV false colour images, being therefore more robust and reliable. These HSV images are built from hyperspectral images (wavelengths from 450 nm to 950 nm and 25 nm of bandwidth) by selecting three channels, one from NIR region and two from the visible region of electromagnetic spectrum. The HSV space allows isolating the colour in a single data dimension to enable a brightness free clustering. An image of a concrete specimen with simulation of biological colonization over a smooth surface is used from a database of hyperspectral images, to evaluate SurfCrete-HSV method. Results show that the SurfCrete-HSV method is reliable for detection of biological colonization on concrete surfaces. The best set of channels to use results from combining one from Near Infra-Red with Red and Blue regions of the electromagnetic spectrum, which reveals high accuracy values with acceptable recall.

Proceedings Article | 26 June 2017 Paper

Detection of cracks on concrete surfaces by hyperspectral image processing

Bruno Santos, Jonatas Valença, Eduardo Júlio

Proceedings Volume 10334, 1033407 (2017) https://doi.org/10.1117/12.2269606

KEYWORDS: Applied research, Detection and tracking algorithms, Image processing, Inspection, Hyperspectral imaging, Image analysis, Mathematical morphology, Image classification

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All large infrastructures worldwide must have a suitable monitoring and maintenance plan, aiming to evaluate their behaviour and predict timely interventions. In the particular case of concrete infrastructures, the detection and characterization of crack patterns is a major indicator of their structural response. In this scope, methods based on image processing have been applied and presented. Usually, methods focus on image binarization followed by applications of mathematical morphology to identify cracks on concrete surface. In most cases, publications are focused on restricted areas of concrete surfaces and in a single crack. On-site, the methods and algorithms have to deal with several factors that interfere with the results, namely dirt and biological colonization. Thus, the automation of a procedure for on-site characterization of crack patterns is of great interest. This advance may result in an effective tool to support maintenance strategies and interventions planning. This paper presents a research based on the analysis and processing of hyper-spectral images for detection and classification of cracks on concrete structures. The objective of the study is to evaluate the applicability of several wavelengths of the electromagnetic spectrum for classification of cracks in concrete surfaces. An image survey considering highly discretized wavelengths between 425 nm and 950 nm was performed on concrete specimens, with bandwidths of 25 nm. The concrete specimens were produced with a crack pattern induced by applying a load with displacement control. The tests were conducted to simulate usual on-site drawbacks. In this context, the surface of the specimen was subjected to biological colonization (leaves and moss). To evaluate the results and enhance crack patterns a clustering method, namely k-means algorithm, is being applied. The research conducted allows to define the suitability of using clustering k-means algorithm combined with hyper-spectral images highly discretized for crack detection on concrete surfaces, considering cracking combined with the most usual concrete anomalies, namely biological colonization.

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