DenseHillNet: a lightweight CNN for accurate classification of natural images

• Published in: PeerJ Computer Science
• Main Author: Saqib S.M.; Asghar M.Z.; Iqbal M.; Al-Rasheed A.; Khan M.A.; Ghadi Y.; Mazhar T.
• Format: Article
• Language: English
• Published: PeerJ Inc. 2024
• Online Access: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85194107878&doi=10.7717%2fPEERJ-CS.1995&partnerID=40&md5=dc5ba4da33ef36ee83aa5ffc818534f4

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. © 2024 Saqib et al. Distributed under Creative Commons CC-BY 4.0. All Rights Reserved.