© 2021 IEEE.In this study, the methods of deep learningbased detection and recognition of threats, evaluated in terms of military and defense industry, using Raspberry Pi platform by unmanned aerial vehicles (UAV) are presented. In the proposed approach, firstly, the training for machine learning on the objects is carried out using convolutional neural networks, which is one of the deep learning algorithms. By choosing the Faster-RCNN and SSD MobileNet V2 architectures of the deep learning method, it is aimed to compare the achievements of the accuracy at the end of the training. In order to be used in the training and testing stages of the recommended methods, data sets containing images selected from different weather, land conditions and different time periods of the day are determined. The model for the detection and recognition of the threatening elements is trained, using 3948 images. Then, the trained model was transferred to the Raspberry Pi 4 Model B electronic board. The method of detecting and recognizing the objects is tested with military operation images and records taken by the UAVs via Raspberry Pi Camera V2 module. While an accuracy rate of %91 has been achieved in the Faster-RCNN architecture in object detection and recognition, this rate has been observed as %88 in the SSD MobileNet V2 architecture.