Jiaxiu Dong^1,2,3, Jianhua Liu⁴, Niannian Wang^1,2,3,*, Hongyuan Fang^1,2,3, Jinping Zhang¹, Haobang Hu^1,2,3, Duo Ma^1,2,3

CMES-Computer Modeling in Engineering & Sciences, Vol.128, No.2, pp. 541-564, 2021, DOI:10.32604/cmes.2021.015875

Abstract Nowadays, asphalt road has dominated highways around the world. Among various defects of asphalt road, cracks have been paid more attention, since cracks often cause major engineering and personnel safety incidents. Current manual crack inspection methods are time-consuming and labor-intensive, and most segmentation methods cannot detect cracks at the pixel level. This paper proposes an intelligent segmentation and measurement model based on the modified Mask R-CNN algorithm to automatically and accurately detect asphalt road cracks. The model proposed in this paper mainly includes a convolutional neural network (CNN), an optimized region proposal network (RPN), a region of interest (RoI) Align… More >

Victor Hugo Costa de Albuquerque¹, Tarique da Silvira Cavalcante², Pedro Pedrosa Rebouças Filho², João Manuel R. S. Tavares¹

The International Conference on Computational & Experimental Engineering and Sciences, Vol.9, No.1, pp. 23-24, 2009, DOI:10.3970/icces.2009.009.023

Abstract Mechanical tests for measurement of hardness are fundamental to evaluate some metallic materials properties, like wear resistance, ductility and flow tension. The manual process for hardness measurement is very interpretive and subjective, as the reading of needed values (diameters or diagonals) of the indenter on the material in test depends directly on operator's viewpoint (parallax effect), appropriate calibration of test machine and operator's experience. Additionally, one must notice that operator's fatigue or tiredness, due to a high number of tests done, is highly prone to measurement errors. \newline In this context, the main goals of this work were the development… More >