Background: The demand for autonomous and intelligent surveillance systems has grown due to rising security concerns and the need for efficient area monitoring. Traditional manual methods lack scalability and responsiveness. Unmanned aerial vehicles (UAVs), combined with AI-driven computer vision, offer a transformative solution for real-time surveillance and threat detection. Objective: This study proposes the design and development of an autonomous drone patrolling system capable of real-time object and human detection, dynamic route adaptation, and autonomous navigation to improve surveillance efficiency. Methods: The system integrates the DroneKit Python library for high-level autonomous control and an STM32 microcontroller for low-level flight management. A live video stream from the onboard camera is processed using the MobileNet-SSD (Single Shot MultiBox Detector) deep learning model for real-time object classification. Predefined GPS waypoints guide the patrol route, while dynamic path adjustments occur in response to detected activity. Core components include OpenCV for image processing, GPS and telemetry modules for localization and communication, and an embedded control system for flight operations. Results: The implementation demonstrates effective autonomous patrolling and object detection with responsive behavior to environmental inputs. MobileNet-SSD offers reliable, low-latency classification with efficient resource usage. GPS-based path adaptation enables accurate re-routing upon human or object detection. Conclusions: The proposed framework delivers a scalable, intelligent solution for autonomous surveillance. By integrating deep learning with GPS control and embedded systems, the model enhances situational awareness for applications in security, emergency response, and area monitoring.