In response to the increasing use of drones, there is an emerging need for dependable security surveillance systems capable of detecting them. This article presents a theoretical framework for drone detection using the YOLOv5x (You Only Look Once) deep learning algorithm, which aims to improve security surveillance systems. The framework is composed of various hardware and software components, including drones, cameras, and computer systems, and uti-lizes YOLOv5x to accurately detect drones. To carry out the research, the Roboflow Drone 1 dataset is used, which includes a variety of images captured under various experimental conditions. The YOLOv5x algorithm is selected due to its high accuracy and low computational cost, which makes it an ideal solution for security surveillance systems. To measure the performance of the system, metrics such as F1-Score, Recall, Precision, Area under the Curve (AUC), and Mean Average Precision (mAP) are employed. The mAP is calculated at 0.95 at a learning rate of 0.5, precisionis 0.901, recall is 0.97, AUC is 95.3, and F-1 score is 0.94. Overall, this framework provides a dependable and efficient approach for detecting drones, resulting in improved security of critical infrastructure, public events, and other sensi-tive locations. Furthermore, the proposed research is compared with existing state-of-the-artwork, and the experi-mental results verified that the proposed research outperformed the state-of-the-art.