Saqib Sheikh Muhammad, Zubair Asghar Muhammad, Iqbal Muhammad, Al-Rasheed Amal, Amir Khan Muhammad, Ghadi Yazeed, Mazhar Tehseen
Institute of Computing and Information Technology, Gomal University, D.I.Khan, Pakistan.
Department of Information Systems, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia.
PeerJ Comput Sci. 2024 Apr 22;10:e1995. doi: 10.7717/peerj-cs.1995. eCollection 2024.
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.
对冰川和山脉等自然图像的检测在交通自动化和户外活动中具有实际应用价值。卷积神经网络（CNN）已被广泛应用于图像识别和分类任务。虽然先前的研究集中在水果、山体滑坡和医学图像上，但仍需要对自然图像的检测，特别是冰川和山脉的检测进行进一步研究。为了解决传统CNN的局限性，如梯度消失和需要许多层，本研究提出了一种名为DenseHillNet的新型模型。该模型采用DenseHillNet架构，一种具有密集连接层的CNN类型，来准确地将图像分类为冰川或山脉。该模型有助于交通和户外活动自动化技术的发展。本研究使用的数据集包括“冰川”和“山脉”类别各3096张图像。在数据集准备和模型训练中采用了严格的方法，确保了结果的有效性。与先前工作的比较表明，在冰川和山脉图像上训练的DenseHillNet模型比仅使用冰川图像的CNN模型（72%）具有更高的准确率（86%）。我们文章的受众是研究人员和研究生。
