DenseHillNet: a lightweight CNN for accurate classification of natural images

The detection of natural images, such as glaciers and mountains, holds practical applications in transportation automation and outdoor activities. Convolutional neural networks (CNNs) have been widely employed for image recognition and classification tasks. While previous studies have focused on fru...

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Veröffentlicht in:PeerJ. Computer science Jg. 10; S. e1995
Hauptverfasser: Saqib, Sheikh Muhammad, Zubair Asghar, Muhammad, Iqbal, Muhammad, Al-Rasheed, Amal, Amir Khan, Muhammad, Ghadi, Yazeed, Mazhar, Tehseen
Format: Journal Article
Sprache:Englisch
Veröffentlicht: United States PeerJ. Ltd 22.04.2024
PeerJ Inc
Schlagworte:
Classification
CNN
Computational Science
Data Mining and Machine Learning
DenseHillNet
Glaciers
Medical imaging equipment
Neural networks
Taiwan
CNN
DenseHillNet
Computer network & communication
Classification
AI
DL
Alrothim and analysis
Computer aided design
ML
ISSN:2376-5992, 2376-5992
Online-Zugang:Volltext
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Zusammenfassung:The detection of natural images, such as glaciers and mountains, holds practical applications in transportation automation and outdoor activities. Convolutional neural networks (CNNs) have been widely employed for image recognition and classification tasks. While previous studies have focused on fruits, land sliding, and medical images, there is a need for further research on the detection of natural images, particularly glaciers and mountains. To address the limitations of traditional CNNs, such as vanishing gradients and the need for many layers, the proposed work introduces a novel model called DenseHillNet. The model utilizes a DenseHillNet architecture, a type of CNN with densely connected layers, to accurately classify images as glaciers or mountains. The model contributes to the development of automation technologies in transportation and outdoor activities. The dataset used in this study comprises 3,096 images of each of the “glacier” and “mountain” categories. Rigorous methodology was employed for dataset preparation and model training, ensuring the validity of the results. A comparison with a previous work revealed that the proposed DenseHillNet model, trained on both glacier and mountain images, achieved higher accuracy (86%) compared to a CNN model that only utilized glacier images (72%). Researchers and graduate students are the audience of our article.
Bibliographie:ObjectType-Article-1
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ISSN:2376-5992
2376-5992
DOI:10.7717/peerj-cs.1995