Real UAV-Bird Image Classification Using CNN with a Synthetic Dataset
| Autor: | Ergun Erçelebi, Ali Emre Öztürk |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
0209 industrial biotechnology
Technology Computer science QH301-705.5 QC1-999 Corner detection 02 engineering and technology synthetic image generation Synthetic data Image (mathematics) 020901 industrial engineering & automation 0202 electrical engineering electronic engineering information engineering General Materials Science Layer (object-oriented design) Biology (General) Instrumentation QD1-999 Dropout (neural networks) Fluid Flow and Transfer Processes Hyperparameter Contextual image classification business.industry Process Chemistry and Technology Deep learning Physics General Engineering deep learning Pattern recognition Engineering (General). Civil engineering (General) corner detection and nearest three-point selection (CDNTS) Computer Science Applications Chemistry corner detection rotary-wing UAV 020201 artificial intelligence & image processing Artificial intelligence TA1-2040 business CNN image classification |
| Zdroj: | Applied Sciences Volume 11 Issue 9 Applied Sciences, Vol 11, Iss 3863, p 3863 (2021) |
| ISSN: | 2076-3417 |
| DOI: | 10.3390/app11093863 |
| Popis: | A large amount of training image data is required for solving image classification problems using deep learning (DL) networks. In this study, we aimed to train DL networks with synthetic images generated by using a game engine and determine the effects of the networks on performance when solving real-image classification problems. The study presents the results of using corner detection and nearest three-point selection (CDNTS) layers to classify bird and rotary-wing unmanned aerial vehicle (RW-UAV) images, provides a comprehensive comparison of two different experimental setups, and emphasizes the significant improvements in the performance in deep learning-based networks due to the inclusion of a CDNTS layer. Experiment 1 corresponds to training the commonly used deep learning-based networks with synthetic data and an image classification test on real data. Experiment 2 corresponds to training the CDNTS layer and commonly used deep learning-based networks with synthetic data and an image classification test on real data. In experiment 1, the best area under the curve (AUC) value for the image classification test accuracy was measured as 72%. In experiment 2, using the CDNTS layer, the AUC value for the image classification test accuracy was measured as 88.9%. A total of 432 different combinations of trainings were investigated in the experimental setups. The experiments were trained with various DL networks using four different optimizers by considering all combinations of batch size, learning rate, and dropout hyperparameters. The test accuracy AUC values for networks in experiment 1 ranged from 55% to 74%, whereas the test accuracy AUC values in experiment 2 networks with a CDNTS layer ranged from 76% to 89.9%. It was observed that the CDNTS layer has considerable effects on the image classification accuracy performance of deep learning-based networks. AUC, F-score, and test accuracy measures were used to validate the success of the networks. |
| Databáze: | OpenAIRE |
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