Treffer: Improving accuracy in drone detection using Yolov2 compared to region-based convolutional neural networks algorithm.

Improving the accuracy in Drone detection is the major goal of this study. The study made use of the Kaggle dataset as the primary source of data. Two distinct groups, Group I and Group 2, each comprising 20 samples, were utilised in this study. Group I employed the Yolov2, while Group 2 utilised the Region-based Convolutional Neural Networks algorithm. The total sample size for the study was 40. Sample size calculations for statistical analysis, as well as the subsequent performance comparison were conducted and implementation was done using Python. The statistical analysis was carried out using clincalc.com with a statistical power (G-power) set at 85%, alpha (a) at 0.05, beta (13) at 0.2. The analysis primarily focused on comparing the performance of the Yolov2 and Algorithm using accuracy value as the key evaluation metric. In terms of accuracy, Yolov2 (95.458%) outperforms Region-based Convolutional Neural Networks algorithm (91.471%), with a two-tailed, p>0.05 significance value of <.001. In summary, the accuracy of Yolov2 outperforms Region-based Convolutional Neural Networks algorithm accuracy. [ABSTRACT FROM AUTHOR]