# Blind Detection of Broadband Signal Based on Weighted Bi-Directional Feature Pyramid Network
## 1 Introduction
Based on this, Dr. [PERSON] proposed an improved target detection network based on You Only Look Once version 3 (YOLOv3) to further improve the performance of deep learning methods in signal blind detection tasks and demonstrated that the You Only Look Once (YOLO), series of methods [18; 19; 20] outperformed Faster Region Convolutional Neural Network (FRCNN) [21; 22] and traditional energy detection methods [7; 10] on this task. In summary, among the existing blind detection methods for communication signals, the speed of energy-based detection is fast, but its robustness is poor and is affected by noise and fading. On the other hand, depth-based methods are now gradually attracting the research interest of scholars and are still in their infancy, without many published research results, and there is still room for improvement in detection accuracy [10]. Moreover, the existing deep learning-based signal blind detection methods do not achieve good results for testing under low Signal-Noise Ratio (SNR) conditions. This is mainly because, with the addition of complex fading channel conditions and non-smooth undulating noise environments, the signals received at the receiver side are subject to complex and diverse interference. Furthermore, the direct application of existing deep neural network models to signal sequences or their feature maps does not guarantee their robustness. Based on the deep learning methods that have been proven to perform best for the signal blind detection task, this paper will improve the deep learning methods used for signal blind detection in terms of comprehensive performance and especially low signal-to-noise ratio. In this paper, we discuss the problem of blind detection and modulation identification on broadband communication signals based on deep learning. Based on previous research, this paper proposes a blind detection method for wideband signals based on BiFPN and Efficient-Intersection over Union (EIOU). The main contributions of our work are as follows. (1) A system model for transmitting time-domain mixed signals in a non-cooperative reception environment is developed, and the signals are analyzed at the receiver side by short-time Fourier transform (STFT) for time-frequency analysis to extract the normalized time-frequency images and the labels of the relative positions of each modulated signal in the image. (2) A target detection model for time-domain mixed signals is built based on YOLOv5 to learn the features of the signal time-frequency distribution image dataset to train the model. To the best of our knowledge, this paper introduces the YOLOv5 method improved by BiFPN and EIOU to the field of signal blind detection for the first time. (3) Based on the dataset generated in this paper, we validate the proposed method through extensive experiments. The results show that the improved method outperforms the existing methods in terms of comprehensive performance, especially under low SNR conditions. The rest of the paper is organized as follows. Section 2 introduces the signal model, the dataset, and its automatic labeling method. Section 3 presents the structure of the relevant algorithmic model and the description of the model improvement in this paper. Section 4 presents the dataset configuration, performance metrics, and comparative results of our experiments. Finally, the conclusion and future work are given in Section 5.