Safe airspace of airports worldwide is crucial to ensure that passengers, workers, and airplanes are safe from external threats, whether malicious or not. In recent years, several airports worldwide experienced intrusions into their airspace by unmanned aerial vehicles. Based on this observation, there is a need for a reliable detection system capable of detecting unmanned aerial vehicles with high accuracy and integrity. This thesis proposes time redundant triplex parallel diverse convolutional neural network architectures trained to detect unmanned aerial vehicles to address the aforementioned issue. The thesis aims at producing a system capable of real-time performance coupled with previously mentioned networks. The hypothesis in this method will result in lower mispredictions of objects other than drones and high accuracy compared to singular convolutional neural networks. Several improvements to accuracy, lower mispredictions, and faster detection times were observed during the performed experiments with the proposed system. Furthermore, a new way of interpreting the intersection over union results for all neural networks is introduced to ensure the correctness and reliability of results. Lastly, the system produced by this thesis is analyzed from a dependability viewpoint to provide an overview of how this contributes to dependability research.