A Tracking Algorithm for Debris Cloud Fragments Produced by Different Hypervelocity Impacts through Image Processing

Tian, Xin; Wang, Xiao; Song, Yan; Wei, Guoliang; Zeng, Ru

doi:10.1109/taes.2023.3262504

Cited by 2 publications

(1 citation statement)

References 41 publications

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“…As a broad field, (1) computer vision is closely related to image processing, which holds significant scientific research value and promising applications in aerospace, (2) traffic safety, (3) autonomous driving, (4) and healthcare. (5) With the emergence of the convolutional neural network (CNN), (6) neural network methods based on CNN have been rapidly applied to image segmentation tasks, including natural and medical images.…”

Section: Introductionmentioning

confidence: 99%

Automatic Construction of U-Net NetworkBased on Genetic Algorithm for Medical Image Segmentation

Gong,

Mo,

Gan

et al. 2023

Sensors and Materials

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In recent years, U-Net has been widely utilized for the segmentation of medical biological images, demonstrating favorable outcomes. However, determining the optimal U-Net network structure for different datasets remains a challenge, often requiring an extensive architecture search or inefficient integration of various deep models for testing purposes. In this paper, we propose an automatic U-Net network design algorithm, U-Net-GA, based on the genetic algorithm. The algorithm effectively addresses the image discrimination task through the introduction of a new variable-length coding strategy, acceleration components, and genetic operators. The key advantage of the proposed algorithm lies in its "automatic" nature, enabling users to obtain the optimal U-Net network structure for a given image without requiring U-Net domain knowledge. The algorithm's effectiveness is demonstrated by its application to two different types of medical image dataset, namely, colorectal cancer and COVID-19 CT images, and a subsequent comparison with other advanced network structures. Experimental results demonstrate that the proposed algorithm exhibits superior performance compared with existing U-Net networks in terms of segmentation accuracy, Dice coefficient, Jaccard index, and loss index.

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