Abstract: The detection of sea surface unmanned aerial vehicles (UAVs) belongs to the problem of small target detection in the background of sea clutter due to weak echoes, and joint multi-feature detection is an effective way to solve such problems. Aiming at the existing time-frequency (TF) tri-feature detection method, which has too much computational complexity in the feature extraction stage and is difficult to realize real-time detection, this paper proposes a fast TF-map-based multi-feature detection method for sea surface UAVs. First, the segmented FFT is performed on the radar complex echo data, and the computed Doppler amplitude spectrum is aligned and spliced along the Doppler dimension so as to construct a fast time-frequency map. Second, the fast TF map is normalized to achieve clutter suppression and enhancement of the target echoes, and three kinds of time-frequency features are extracted based on the normalized fast TF map. Third, the fast convex hull learning algorithm is utilized to train the decision judgement region under the given false alarm probability. Finally, the effectiveness of the proposed method is validated and analyzed by the measured UAV data.