On the performance evaluation of object classification models in low altitude aerial data
© The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022.
| Veröffentlicht in: | The Journal of supercomputing. - 1998. - 78(2022), 12 vom: 09., Seite 14548-14570 |
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| Format: | Online-Aufsatz |
| Sprache: | English |
| Veröffentlicht: |
2022
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| Zugriff auf das übergeordnete Werk: | The Journal of supercomputing |
| Schlagworte: | Journal Article Convolutional neural networks Deep learning Machine learning Object recognition UAV datasets |
| Zusammenfassung: | © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022. 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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| Beschreibung: | Date Revised 31.08.2024 published: Print-Electronic Citation Status PubMed-not-MEDLINE |
| ISSN: | 0920-8542 |
| DOI: | 10.1007/s11227-022-04469-5 |