Intelligent Maritime Radar Target Detection in Unknown Environment via Self-Learning

Intelligent maritime radar target detectors, developed by deep learning technology, have shown encouraging detection performance. The detectors usually operate assuming that the training set and the test set are independent and identically distributed (i.i.d.). However, variable sea surface conditions can easily disrupt the assumption and significantly impair the detector's performance. This paper introduces the concept of unsupervised domain adaptation (UDA) to formulate the problem of detection in an unknown environment and proposes a UDA method, i.e., SL−UDA, based on the self-learning strategy to deal with it. The SL−UDA enables the detector to efficiently adapt to unknown environments while retaining previous knowledge gained from the existing data as much as possible. Specifically, the SL−UDA first trains the detector on the annotated data. Then, when the detector encounters an unknown environment, the SL-UDA generates the pseudo labels for current data by integrating adaptive confidence threshold and echo prior characteristics. Finally, the data with both label and pseudo label is used to retrain the detector, making the detector adapt to unknown environments while preserving previous knowledge. Experimental results on public maritime radar database show that the performance of the SL−UDA method is better than several classical UDA methods.