Wei Zhenhua, Li Zijun, Han Siming
Academy of Operational Support Rocket Force Engineering University, Xi'an, 710025, China.
Sci Rep. 2023 Nov 23;13(1):20614. doi: 10.1038/s41598-023-48030-7.
With the increasing complexity of the shortwave communication environment, the efficiency and accuracy of the manual detection of Morse code no longer meet actual needs. Therefore, this paper proposes a Morse code detection algorithm called YFDM. For the time-frequency image of the received signal, a combination module of deformable convolution and C3 is used to enhance the backbone network's attention to the abstract semantics and location information of Morse code. GSConv and VOV-GSCSP modules are used to build a lightweight neck network. Finally, the confidence propagation cluster (CP-Cluster) algorithm is used to filter the detection frame. In an ablation experiment, the parameters and giga floating-point operations per second (GFLOPs) of YFDM were 5.961 M and 9.74 G, respectively, 15.11% and 38.9% less than those of YOLOv5. Moreover, when WIoUv1 was used as the loss function of the bounding box, the AP0.5:0.95 and frames per second (FPS) values of the algorithm reached the highest values, 0.68 and 72.4. The experimental results indicate that the algorithm can effectively reduce the weight of the model while ensuring the detection accuracy and inference speed.
随着短波通信环境日益复杂，手动检测莫尔斯电码的效率和准确性已无法满足实际需求。因此，本文提出了一种名为YFDM的莫尔斯电码检测算法。对于接收信号的时频图像，采用了可变形卷积和C3的组合模块，以增强骨干网络对莫尔斯电码抽象语义和位置信息的关注。使用GSConv和VOV-GSCSP模块构建了一个轻量级颈部网络。最后，使用置信传播聚类（CP-Cluster）算法对检测框进行过滤。在消融实验中，YFDM的参数和每秒千兆浮点运算次数（GFLOPs）分别为5.961M和9.74G，比YOLOv5分别少15.11%和38.9%。此外，当使用WIoUv1作为边界框的损失函数时，该算法的AP0.5:0.95和每秒帧数（FPS）值达到最高，分别为0.68和72.4。实验结果表明，该算法在确保检测精度和推理速度的同时，能够有效降低模型权重。
