Detection of object (Weapons) with deep learning algorithms from images obtained by unmanned aerial vehicles

Today, the rapid development of Artificial Intelligence technologies is effective in the success of deep learning algorithms in different application areas. These applications detect many objects that even the human eye cannot detect in object detection in videos and images with deep learning algorithms.In this study, it is aimed to detect weapons from images obtained from Unmanned Aerial Vehicle (UAV) by using deep learning algorithms. Images were obtained from the UAV at 200 different angles and heights. Images from different angles and heights obtained from the unmanned aerial vehicle are trained by Regional Based Convolutional Neural Networks (R-CNN) and Residual Neural Network (ResNet). Twothirds of the images we obtained were split into training images and one-third into test images. The feature maps extracted from the images used for training were compared with the test images. By bringing these compared images closer to the desired images, 99% of the desired image detection is achieved. Performance evaluation of the algorithms was made using Loss plot, mAP curves, Precision, Recall and F1-Score. The performance evaluation of the detected images is discussed, and the success of deep learning algorithms used in object detection is presented. The ResNet model showed higher performance with 64% accuracy, 94% recall and 76% F1 score.