Drones in today’s life are used in many sectors to automate various tasks. Delivering small items, capturing live events, and surveying dangerous areas are a few incredible operations of drones in today’s society. It can perform many tasks in human-restricted areas with less service time and cost. Drones are a rapidly growing demand for less human interaction in the automobile sector. Using drones in maintenance service is an excellent concept to automate many maintenance and cleaning activities. In areas that are easily accessible, such as single-story buildings or public places, manual maintenance or cleaning is a cheap and simple option. However, there are a few situations where areas which are to be cleaned are mounted at high elevations on poles or at crowded places, and other environments where the contamination is extensive and continuous, requiring a very high cleaning frequency. Manual cleaning in such environments is a challenging task and Automated cleaning devices play a vital role in cleaning activities located at these complex places. This thesis work studies an open-source development drone used to make a drone prototype system and analyzed its performance that can be used to maintain and clean outdoor surveillance cameras. This automation reduces human effort, service time, and cost. It describes how to build an object detection model to detect dome cameras by using Haarcascade classifier in cascade GUI. The drone is installed with an automated water spray mechanism turned on when it recognizes the surveillance camera lenses. The object detection and water sprayings tasks are done using Raspberry pi onboard computer. The integration of Raspberry pi and Pixhawk flight controller is done using the MAVlink communication protocol. The object detection model and the water spraying mechanism of the prototype are tested and analyzed by appropriate metrics. The product is assessed and analysed by capturing data traffic of the heartbeat messages between Raspberry pi and Pixhawk flight controller while the drone is functional and the 95 percent confidence interval of data transmission is found.