ESTIMATION OF VOLUME OF A SOLID OBJECT FROM THREE
      DIMENSIONAL POINT CLOUDS GENERATED BY CONVOLUTIONAL
      NEURAL NETWORKS BASED SEMANTIC SEGMENTATION

AbstractConvolutional neural networks were used for multiclass segmentation in thermal infrared face analysis. The principle is based on existing image-to-image translation approaches, where each pixel in an image is assigned to a class label. We show that established networks architectures can be trained for the task of multiclass face analysis in thermal infrared. Created class annotations consisted of pixel-accurate locations of different face classes. Subsequently, the trained network can segment an acquired unknown infrared face image into the defined classes. Furthermore, face classification in live image acquisition is shown, in order to be able to display the relative temperature in real-time from the learned areas. This allows a pixel-accurate temperature face analysis e.g. for infection detection like Covid-19. At the same time our approach offers the advantage of concentrating on the relevant areas of the face. Areas of the face irrelevant for the relative temperature calculation or accessories such as glasses, masks and jewelry are not considered. A custom database was created to train the network. The results were quantitatively evaluated with the intersection over union (IoU) metric. The methodology shown can be transferred to similar problems for more quantitative thermography tasks like in materials characterization or quality control in production.

Download Full-text

Generation and Annotation of Simulation-Real Ship Images for Convolutional Neural Networks Training and Testing

Applied Sciences ◽

10.3390/app11135931 ◽

2021 ◽

Vol 11 (13) ◽

pp. 5931

Author(s):

Ji’an You ◽

Zhaozheng Hu ◽

Chao Peng ◽

Zhiqiang Wang

Keyword(s):

Neural Networks ◽

Convolutional Neural Networks ◽

Image Annotation ◽

Three Dimensional ◽

Image Data ◽

Detection Algorithm ◽

Simulation Software ◽

High Quality ◽

Annotation Method ◽

Detection Of Objects

Large amounts of high-quality image data are the basis and premise of the high accuracy detection of objects in the field of convolutional neural networks (CNN). It is challenging to collect various high-quality ship image data based on the marine environment. A novel method based on CNN is proposed to generate a large number of high-quality ship images to address this. We obtained ship images with different perspectives and different sizes by adjusting the ships’ postures and sizes in three-dimensional (3D) simulation software, then 3D ship data were transformed into 2D ship image according to the principle of pinhole imaging. We selected specific experimental scenes as background images, and the target ships of the 2D ship images were superimposed onto the background images to generate “Simulation–Real” ship images (named SRS images hereafter). Additionally, an image annotation method based on SRS images was designed. Finally, the target detection algorithm based on CNN was used to train and test the generated SRS images. The proposed method is suitable for generating a large number of high-quality ship image samples and annotation data of corresponding ship images quickly to significantly improve the accuracy of ship detection. The annotation method proposed is superior to the annotation methods that label images with the image annotation software of Label-me and Label-img in terms of labeling the SRS images.

Download Full-text

Identification of Mode Shapes of a Composite Cylinder Using Convolutional Neural Networks

Materials ◽

10.3390/ma14112801 ◽

2021 ◽

Vol 14 (11) ◽

pp. 2801

Author(s):

Bartosz Miller ◽

Leonard Ziemiański

Keyword(s):

Neural Networks ◽

Convolutional Neural Networks ◽

Composite Structures ◽

Three Dimensional ◽

Mode Shapes ◽

Identification Algorithm ◽

Material Degradation ◽

Local Material ◽

Noisy Input ◽

Shape Vector

The aim of the following paper is to discuss a newly developed approach for the identification of vibration mode shapes of multilayer composite structures. To overcome the limitations of the approaches based on image analysis (two-dimensional structures, high spatial resolution of mode shapes description), convolutional neural networks (CNNs) are applied to create a three-dimensional mode shapes identification algorithm with a significantly reduced number of mode shape vector coordinates. The CNN-based procedure is accurate, effective, and robust to noisy input data. The appearance of local damage is not an obstacle. The change of the material and the occurrence of local material degradation do not affect the accuracy of the method. Moreover, the application of the proposed identification method allows identifying the material degradation occurrence.

Download Full-text