Wyniki wyszukiwania - BazTech

1

Towards ensuring software interoperability between deep learning frameworks

Lee Youn Kyu, Park Seong Hee, Lim Min Young, Lee Soo-Hyun, Jeong Jongwook

Journal of Artificial Intelligence and Soft Computing Research

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2023

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Vol. 13, No. 4

215--228

EN

With the widespread of systems incorporating multiple deep learning models, ensuring interoperability between target models has become essential. However, due to the unreliable performance of existing model conversion solutions, it is still challenging to ensure interoperability between the models developed on different deep learning frameworks. In this paper, we propose a systematic method for verifying interoperability between pre- and post-conversion deep learning models based on the validation and verification approach. Our proposed method ensures interoperability by conducting a series of systematic verifications from multiple perspectives. The case study confirmed that our method successfully discovered the interoperability issues that have been reported in deep learning model conversions.

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A systematic review of the automatic kidney segmentation methods in abdominal images

Pandey Mohit, Gupta Abhishek

Biocybernetics and Biomedical Engineering

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2021

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Vol. 41, no. 4

1601--1628

EN

Background and Purpose: The precise kidney segmentation is very helpful for diagnosis and treatment planning in urology, by giving information about malformation in the shape and size of the kidney. Kidney segmentation in abdominal computed tomography (CT) images provides support for the efficient and effortless detection of kidney tumors or cancers. Manual kidney segmentation is time-consuming and not reproducible. To overcome this problem, computer-aided automatic approach is used for kidney segmentation. The purpose of presenting this review paper is to analyze different automatic kidney segmentation methods in abdominal CT scans. Materials and Methods: PRISMA guidelines were used to conduct the systematic review. To acquire related articles, three online open source databases were used and a query was formed with relevant keywords. On the basis of inclusion and exclusion criteria, relevant papers were selected from the search results for finding answers to the four evolved research questions. Results: The results reported in the different studies were analyzed based on the formulated research questions. The challenges of these studies were listed to overcome in the future. Many performance parameters representing the results like Hausdorff Distance (HD) and Dice Similarity Coefficient (DSC) were compared among the relevant studies. Conclusion: The systematic review article consists of the essence of the several computer-aided kidney segmentation methods using abdominal CT images, which are dedicated to answering the evolved research questions like various methods, accuracy, datasets size, various challenges, and the effect of pathological kidney on the performance of segmentation method had been discussed.

3

A Study of Correlation between Fishing Activity and AIS Data by Deep Learning

Shen K. Y., Chu Y. J., Chang S. J., Chang S. M.

TransNav : International Journal on Marine Navigation and Safety of Sea Transportation

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2020

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Vol. 14. no. 3

527--531

EN

Previous researches on the prediction of fishing activities mainly rely on the speed over ground (SOG) as the referential attribute to determine whether the vessel is navigating or in fishing operation. Since more and more fishing vessels install Automatic Identification System (AIS) either voluntarily or under regulatory requirement, data collected from AIS in real time provide more attributes than SOG which may be utilized to improve the prediction. To be specific, the ships' trajectory patterns and the changes in course become available and should be considered. This paper aims to improve the accuracy in the identification of fishing activities. First, we do feature extraction from the AIS data of coastal waters around Taiwan and build a Recurrent Neural Network (RNN) model. Then, the activity data of fishing vessels are divided into fishing and non-fishing. Finally, based on the testing by feeding various fishing activity data, we can identify the fishing status automatically.

4

Ship recognition and tracking system for intelligent ship based on deep learning framework

Liu B., Wang S. Z., Xie Z. X., Zhao J. S., Li M. F.

TransNav : International Journal on Marine Navigation and Safety of Sea Transportation

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2019

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Vol. 13. no. 4

699--705

EN

Automatically recognizing and tracking dynamic targets on the sea is an important task for intelligent navigation, which is the prerequisite and foundation of the realization of autonomous ships. Nowadays, the radar is a typical perception system which is used to detect targets, but the radar echo cannot depict the target’s shape and appearance, which affects the decision-making ability of the ship collision avoidance. Therefore, visual perception system based on camera video is very useful for further supporting the autonomous ship navigational system. However, ship’s recognition and tracking has been a challenge task in the navigational application field due to the long distance detection and the ship itself motion. An effective and stable approach is required to resolve this problem. In this paper, a novel ship recognition and tracking system is proposed by using the deep learning framework. In this framework, the deep residual network and cross-layer jump connection policy are employed to extract the advanced ship features which help enhance the classification accuracy, thus improves the performance of the object recognition. Experimentally, the superiority of the proposed ship recognition and tracking system was confirmed by comparing it with state of-the-art algorithms on a large number of ship video datasets.