Small-Target Ship Detection with Joint Spatio-Temporal Features Across Multiple Frames

Detecting small ship targets in sea-sky background environments is challenging due to interference from clouds, islands, sea clutter, and the limited spatial information in long-range infrared imagery. To address these issues, this paper proposes a robust detection framework that integrates multi-scale spatial feature enhancement with temporal trajectory analysis. First, a candidate target extraction method based on a multi-scale differential histogram of oriented gradients is introduced. By exploiting gradient distribution differences between targets and surrounding backgrounds, our method effectively enhances target responses while suppressing structured background edges. This response is further fused with a log-spectrum-based saliency map to improve target contrast and reduce clutter. Next, a candidate trajectory extraction algorithm based on inverse optical flow matching is developed to utilize temporal consistency. Optical flow-based grayscale compensation predicts target intensity changes between frames, while Kalman filtering estimates motion states and performs trajectory association. Finally, a multi-feature trajectory filtering strategy is designed, combining motion entropy stability, peak signal-to-noise ratio, and trajectory lifecycle to distinguish true targets from false alarms. Experimental results on eight infrared maritime sequences demonstrate superior performance. The proposed method achieves an average Background Suppression Factor (BSF) of 45.2 and an average Signal-to-Clutter Ratio Gain (SCRG) of 22.3 × 10³, representing a substantial improvement over all baseline algorithms. Receiver Operating Characteristic analysis further confirms a mean detection rate exceeding 90% at a false-alarm rate of 10^-3 across all sequences, confirming improved detection performance and robustness in complex maritime environments.