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|a 10.1007/s11227-022-04469-5
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|a eng
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| 100 |
1 |
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|a Mittal, Payal
|e verfasserin
|4 aut
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| 245 |
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|a On the performance evaluation of object classification models in low altitude aerial data
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|c 2022
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|a Text
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|a ƒaComputermedien
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|a ƒa Online-Ressource
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|a Date Revised 31.08.2024
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|a published: Print-Electronic
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|a Citation Status PubMed-not-MEDLINE
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|a © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022.
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|a This paper compares the classification performance of machine learning classifiers vs. deep learning-based handcrafted models and various pretrained deep networks. The proposed study performs a comprehensive analysis of object classification techniques implemented on low-altitude UAV datasets using various machine and deep learning models. Multiple UAV object classification is performed through widely deployed machine learning-based classifiers such as K nearest neighbor, decision trees, naïve Bayes, random forest, a deep handcrafted model based on convolutional layers, and pretrained deep models. The best result obtained using random forest classifiers on the UAV dataset is 90%. The handcrafted deep model's accuracy score suggests the efficacy of deep models over machine learning-based classifiers in low-altitude aerial images. This model attains 92.48% accuracy, which is a significant improvement over machine learning-based classifiers. Thereafter, we analyze several pretrained deep learning models, such as VGG-D, InceptionV3, DenseNet, Inception-ResNetV4, and Xception. The experimental assessment demonstrates nearly 100% accuracy values using pretrained VGG16- and VGG19-based deep networks. This paper provides a compilation of machine learning-based classifiers and pretrained deep learning models and a comprehensive classification report for the respective performance measures
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|a Journal Article
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| 650 |
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|a Convolutional neural networks
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| 650 |
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|a Deep learning
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| 650 |
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|a Machine learning
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| 650 |
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|a Object recognition
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| 650 |
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|a UAV datasets
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| 700 |
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|a Sharma, Akashdeep
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Singh, Raman
|e verfasserin
|4 aut
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| 700 |
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|a Sangaiah, Arun Kumar
|e verfasserin
|4 aut
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|i Enthalten in
|t The Journal of supercomputing
|d 1998
|g 78(2022), 12 vom: 09., Seite 14548-14570
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|x 0920-8542
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|g volume:78
|g year:2022
|g number:12
|g day:09
|g pages:14548-14570
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|u http://dx.doi.org/10.1007/s11227-022-04469-5
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