This paper introduces a novel approach to ship monitoring for enhanced maritime safety and security. Traditional methods rely on Automatic Identification Systems (AIS) and marine radar, but their effectiveness is hindered by the absence of AIS on some vessels. To overcome this limitation, Faster R-CNN, trained on Range-Compressed Airborne Radar Data, is proposed. By utilizing airborne radar signals, the need for AIS installations is eliminated. The Faster R-CNN algorithm is trained on both Time Domain and Doppler Domain data types for object detection and classification, respectively. Leveraging Resnet50 as the backbone model, the system achieves efficient ship detection by analyzing specific regions, thus reducing false detections. This innovative approach presents a significant advancement in sea monitoring capabilities, ensuring enhanced safety and security at sea.