Explainable artificial intelligence models for enhancing classification reliability of ground weapon systems
| Autor: | Gimin Bae, Janghyong Lee |
|---|---|
| Jazyk: | English<br />Korean |
| Rok vydání: | 2023 |
| Předmět: | |
| Zdroj: | 선진국방연구, Vol 6, Iss 3 (2023) |
| Druh dokumentu: | article |
| ISSN: | 2635-5531 2636-1329 |
| DOI: | 10.37944/jams.v6i3.216 |
| Popis: | This study focused on the development of a reliable artificial intelligence (AI) model to enhance the classification reliability of ground weapon systems for surveillance and reconnaissance applications. The proposed AI model overcomes the limited data availability of military objects such as tanks, canons, and multiple-launch rockets by leveraging transfer learning and fine-tuning techniques. A comprehensive evaluation of 35 deep learning models using the publicly available Military-Vehicles dataset on Kaggle identified MobileNet as the most suitable model for ground weapon system classification. The selected MobileNet model achieved an average F1 score of 92% when tested on a dataset comprising five types of ground-weapon systems. In addition, the application of the explainable AI technique Grad-CAM provided insights into the decision-making process of the proposed model and verified its reliability. Real-world evaluations using frames extracted from training videos demonstrated promising accuracy for tanks, canons, and multiple-launch rockets. However, challenges related to object occlusion and the absence of target objects in the images were observed, which resulted in misclassifications. Overall, this study contributes to the development of explainable and reliable AI models for enhancing the performance of ground surveillance and reconnaissance systems. |
| Databáze: | Directory of Open Access Journals |
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