Wideband signal detection is an important problem in wireless communication. With the rapid development of deep learning (DL) technology, some DL-based methods are applied to wireless communication and have shown great potential. In this paper, we present a novel neural network for detecting signals and classifying signal types in wideband spectrograms. Our network utilizes the key point estimation to locate the rough centerline of the signal region and recognize its class. Then, several regressions are carried out to obtain properties, including the local offset and the border offsets of a bounding box, which are further synthesized for a more fine location. Experimental results demonstrate that our method performs more accurate than other DL-based object detection methods previously employed for the same task. In addition, our method runs obviously faster than existing methods, and it abandons the candidate anchors, which make it more favorable for real-time applications.

中文翻译：

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深度卷积网络用于频谱图中的多类型信号检测和分类

宽带信号检测是无线通信中的重要问题。随着深度学习（DL）技术的飞速发展，一些基于DL的方法已应用于无线通信，并显示出巨大的潜力。在本文中，我们提出了一种用于在宽带频谱图中检测信号和分类信号类型的新型神经网络。我们的网络利用关键点估计来定位信号区域的粗略中心线并识别其类别。然后，进行几次回归以获得包括边界框的局部偏移和边界偏移在内的属性，将其进一步合成以获得更精确的位置。实验结果表明，与先前用于同一任务的其他基于DL的对象检测方法相比，我们的方法性能更高。此外，