Detection of Object (Weapons) With Deep Learning Algorithms From İmages Obtained By Unmanned Aerial Vehicles

Abstract

Today, the rapid development of Artificial Intelligence technologies is effective in the success of deep learning algorithms in different application areas. In addition to these applications, it detects many objects that cannot be noticed even with the human eye in object detection in video and images with deep learning algorithms. In this study, it was aimed to detect weapons by using images obtained from UAV using deep learning algorithms. Regional Based Convolutional Neural Networks and Residual Network were used and the performance evaluation of the architecture of these algorithms was presented. Performance evaluation of algorithms was made using Loss plots, Precision-Recall, and mAP curves. In this study, 200 images of different angles and heights were obtained from the unmanned aerial vehicle. Two-thirds of the images we obtained were divided into training and one-third test images. Resnet and Region Based Convolutional neural networks used in test images have been successful in object detection. Regional based neural networks and Residual Network that we use for object detection are used. Images from different angles and heights obtained from the unmanned aerial vehicle are trained using regional-based neural networks and residual network algorithms. 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, the desired image detection was detected at a rate of 99%. The performance evaluation of the detected images is discussed and the success of the deep learning algorithms used in object detection is presented. Considered in the performance evaluation of deep learning algorithms, Loss Charts (Error), Precision-Recall curves show the accuracy of detection in a short time. Algorithms that will increase the possibilities and capabilities of unmanned aerial vehicles used especially in border security and internal security are considered to increase in the future.