Radar Target Detection (RTD) is considered to be one of the most essential parts of modern radar systems. In typical radars, detecting targets in noise is difficult. Conventional radar signal processing approaches such as Constant False Alarm Rate (CFAR) are adopted in an attempt to improve the Signal-to-Noise Ratio (SNR). However, due to the severity of the harsh and complex environments in the radar measurements, the target detection problem becomes extremely challenging when employing such traditional approaches. Therefore, developing a reliable and robust RTD technique is essential. In this paper, an augmented Doppler Filter Bank (DFB) based approach has been proposed to handle the associated radar drawbacks in such a complicated scenario, by incorporating the computer vision algorithms in order to separate the moving targets from the noisy background through a real radar dataset. A Frequency Modulated Continuous Wave (FMCW) radar has been mounted on an Unmanned Aerial Vehicle (UAV) for ground targets detection purposes. A real flight has been conducted in a challenging environment to assess the performance of the proposed system. The experimental results demonstrate the ability of the proposed system to enhance the estimated forward velocity to 82.8% over the conventional DFB with the CFAR detector.