LAUSR

Short-wave radio is an indispensable long-distance means of communication, among which Morse signals, which rely on simplicity and efficiency, plays an import role in military and civilian applications. Automatic Morse detection and recognition have been researched for several years, but some thorny problems in actual communication always restrict the performance of methods. In this article, by introducing deep learning technology, we propose a network named MorseNet that can simultaneously locate and decode Morse signals in the spectrogram. MorseNet uses shared convolutions to extract shared features for both the detection and recognition branches. The detection branch regresses bounding boxes based on signal centerlines, and the recognition branch decodes Morse fragments cropped from feature maps by a convolutional recurrent neural network (CRNN). The losses of two branches are combined to implement the end-to-end training. Experimental results on four “simulated Morse + real background” datasets demonstrate that the proposed method achieves state-of-the-art performance in both detection and recognition, and it effectively improves four problems that have long been troublesome in accomplishing the tasks. Furthermore, the joint training strategy and architecture give MorseNet advantages over its two-stage deployment in terms of accuracy, speed, and model size.