Lock-in thermography is a well-established non-destructive technique for detecting defects in composite materials. The qualitative analysis of defects is a challenging task and usually is assessed by an expert operator after the application of suitable algorithms. In this regard, deep learning algorithms are very attractive since they allow to speed up and automatize the identification and characterization of defects. In light of this consideration, the aim of this work is to investigate the influence of lock-in thermography set-up parameters on the capability of a temporal convolutional neural network to characterize defects in a carbon fiber-reinforced polymer specimen. Moreover, to make the lock-in technique suitable for industrial applications, a comprehensive study of reducing both the experimental test time and the processing time has been carried out. The performance of the CNN has been evaluated as a function of some lock-in test parameters such as the number of acquired frames per cycles and the number of excitation cycles. The obtained results have been critically discussed through qualitative and quantitative analyses.
The analysis of innovative materials and processes stands at the frontier of a series of wide-ranging scientific problems and poses stimulating challenges from a scientific as well as technological point of view, by virtue of its connection with various industrial sectors, such as aerospace and aeronautics. In recent years, composite materials have found numerous applications due to their mechanical characteristics and properties, representing the evolution of materials science and technologies by fusing within them the best characteristics of multiple materials. The present work is focused on the characterization of composite materials, using non-destructive techniques (NDT), to check different kinds of defects eventually present for a quality control of the object under observation. Shearography and thermography are used as nondestructive methods. The former, is an optical interferometric method for the detection of surface or sub-surface defects, the latter is a diagnostic technique that, by measuring the infrared radiation emitted by a body, allows to determine its surface temperature and to understand the health status of the investigated object. The results of the shearography technique, including, are complementary to thermographic techniques and allow us to have a complete characterization of the object. Their use offers advantages related to visualization and testing of end products, as well as the noncontact nature, nondestructive and areal working principle, rapid response, high sensitivity, resolution, and accuracy.
KEYWORDS: Image segmentation, Semantics, RGB color model, Deep learning, Cameras, Decision trees, Education and training, Machine learning, Data modeling, Agriculture
In-field sensing systems for automatic yield monitoring are gaining increasing importance as they promise to give a considerable boost in production. The development of artificial intelligence and sensing technologies to assist the human workforce also meets sustainability needs, which impact the ecological goals of current and future agricultural processes. In this context, image acquisition and processing systems are widely adopted to extract useful information for farmers. Although RGB-D cameras have been used in many applications for ground-based proximal sensing, relatively few works can be found that include depth information in image analysis. In this work, both semantic and depth information from RGB-D vineyard images is used in processing pipeline composed of a decision tree algorithm and a deep learning model. The goal is to reach coherent semantic segmentation of a set of natural images acquired at both long and short distances, using a low-cost RGB-D camera in an experimental vineyard. Depth information of each image is fed into a decision tree to predict the distance of the acquired vines from the camera. Before feeding the deep learning models, the images to be segmented are manipulated according to the predicted distance. The results of semantic segmentation with and without using the decision tree are compared, showing how depth information appears to be highly relevant in enhancing the accuracy and precision of the predicted semantic maps.
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