Pavement Recognition Based on Improving VGG16 Network Model

doi:10.25236/ijndes.2023.070304

Cited by 3 publications

(2 citation statements)

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“…Machine learning technology has also been widely used in fire image classification, where researchers have utilized machine learning algorithms such as support vector machines (SVM) and random forests to classify and identify fire images [8]. These algorithms can automatically learn the features of flame images and classify and recognize them with high accuracy [9,10].…”

Section: Introductionmentioning

confidence: 99%

Fire image detection and classification analysis based on VGG16 image processing model

Hou

2024

ACE

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Fire image classification technology refers to the classification and recognition of fire images through computer vision technology in order to take timely countermeasures. With the development of computer vision technology, fire image classification technology has been widely studied and applied. Deep learning techniques have achieved great success in the field of image classification, with researchers classifying and identifying fire images by using deep learning models such as convolutional neural networks. This paper introduces the research background and application of fire image classification technology, and the experimental results of detection and classification analysis of fire image based on vgg16 image processing model. The model can classify and identify fire images well and achieve good prediction effect. The accuracy of training set and test set is stable at 99%, the accuracy and accuracy of the model reach 98%, and the recall rate and F1 score reach 99%. The application of fire image classification technology is of great significance. By using computer vision technology to classify and identify fire images, it can improve the accuracy and efficiency of fire monitoring and early warning system, timely detect and control fire, and reduce the loss caused by fire. At the same time, the continuous development of deep learning technology also provides a broader space for the research and application of fire image classification technology.

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Section: Introductionmentioning

confidence: 99%

Fire image detection and classification analysis based on VGG16 image processing model

Hou

2024

ACE

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“…These questionnaires include questions on user satisfaction, emotional response, attention, etc., which can help researchers understand the subjective experience and feelings of users [9]. By analyzing the results of these questionnaires, researchers can find out what users' preferences and needs are for different aspects of virtual environments, so as to further optimize the design of virtual environments and the way of interaction, and to improve the users' experience [10].…”

Section: Introductionmentioning

confidence: 99%

Research and recommendations on user immersion in Virtual Reality (VR) technology based on data mining

Peng

2024

ACE

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This paper describes the applications and challenges of virtual reality (VR) technology, with a focus on how to improve users' sense of experience in VR environments. In order to better understand users' sense of experience in VR environments, this paper uses a variety of methods for data analysis. The findings show that the older and longer the duration, the more immersive the user is and the higher the immersion with Oculus Rift. 30-36 year old males, 32-35 year old females, and 34-40 year olds experienced higher immersion, while 34-44 year olds had the lowest motion sickness but the best experience. Men with HTC Vive experience higher immersion, while women and others with Oculus Rift experience higher immersion. According to the study, the HTC Vive is best for users under the age of 30, the Playstation VR is best for users aged 30-50, and the Oculus Rift is best for users over 50. In terms of duration, the Oculus Rift provides the best experience for users under 10 minutes and over 40 minutes, while the HTC Vive is better in the 10-40 minute duration. Male users are most immersed at 23-31 minutes, female users are most immersed at 23-31 minutes, and other users are most immersed at 26-33 minutes. Motion sickness was lowest, but the best duration of the experience was 31 minutes.HTC Vive users felt higher immersion at 25-32 minutes, Playstation VR users at 21-28 minutes, and Oculus Rift users at 25-34 minutes. These conclusions provide an important reference basis for improving users' experience in the VR environment. At the same time, this paper puts forward targeted recommendations based on the conclusions drawn from the statistical analysis of the data, which can largely help users to improve the sense of VR experience and bring users an immersive virtual reality experience.

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