Article Detail

Mehmet KARATAŞ*

Keywords

monocular volume estimation single camera volume estimation monocular mass estimation mass prediction from images image-based volume estimation

Doi : 10.71350/jner.2026165

Abstract

The accurate and rapid classification of flying objects is of great importance in the field of aviation for security, traffic management, and defense applications. In this study, pre-trained deep learning models were utilized for the classification of objects such as birds, airplanes, helicopters, parachutes, and drones. The models used include AlexNet, EfficientNet-B0, MobileNetV2, and ResNet18. These models were structured using the transfer learning method, where only the final classification layer was trained, and they were trained for 10 epochs. This approach integrated the powerful feature extraction capabilities of the pretrained models into the classification process.e performance of the models was evaluated on the test dataset, and their accuracy rates were compared. The highest test accuracy of 99.10% was achieved by the EfficientNet-B0 model, followed by MobileNetV2 with 98.74% accuracy, and ResNet18 and AlexNet both with 96% accuracy. Furthermore, additional performance analyses were conducted by classifying the feature vectors obtained from the intermediate layers of the pretrained models using traditional machine learning algorithms (K-Nearest Neighbors, Support Vector Machines, Multi-Layer Perceptron, Random Forest, and Decision Tree). The dataset used in the study is an original dataset created from images collected from Kaggle, Roboflow, Google, and various search engines.

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