Drones are among the devices that are used in many different activities. There is a time when drones have accidents and authorities need to find the cause. Drone forensics is used to determine the cause of an accident. The analysis phase of drone forensics is one of the most important steps in determining accident causes. In this paper, we applied a deep-learning technique to classify drone collisions. We investigate the use of Inception V.9 as the deep-learning framework. Additionally, this study compares the performance of the proposed method with other techniques, such as MobileNet, VGG and ResNet, in classifying drone collisions. In this experiment, we also implement transfer learning as well as its fine tuning to speed up the training process and to improve the accuracy value. Additionally, our investigation shows that Inception V.9 outperforms other frameworks in terms of accuracy, precision and F1 score.

Original languageEnglish
Pages (from-to)175-186
Number of pages12
JournalJordanian Journal of Computers and Information Technology
Issue number2
Publication statusPublished - Jun 2023


  • Drone forensics
  • Inception
  • MobileNet
  • Network infrastructure
  • RestNet
  • Transfer learning
  • VGG


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