国产欧美精品一区二区,中文字幕专区在线亚洲,国产精品美女网站在线观看,艾秋果冻传媒2021精品,在线免费一区二区,久久久久久青草大香综合精品,日韩美aaa特级毛片,欧美成人精品午夜免费影视

In addressing the problem of identifying target signals from multiple overlapping electromagnetic signals in both time and frequency domains, a method based on an improved YOLOv5 architecture for electromagnetic signal target recognition is proposed. By enhancing the YOLOv5 algorithm, the detection accuracy of target signals in time-frequency images of electromagnetic signals is improved. MobileNetV3 is used as the backbone network to achieve a balance between precision and speed while ensuring the backbone network remains lightweight. The adaptive spatial feature fusion (ASFF) module is introduced into the feature pyramid network module to merge features of different scales and adjust the loss function, thereby enhancing the network's ability to perceive targets of various scales. The improved YOLOv5 algorithm is compared with the original YOLOv5 algorithm and the SSD algorithm on a custom electromagnetic signal time-frequency image dataset. Experiments show that the improved method significantly enhances the detection capability of target signals in time-frequency images, demonstrating practical value.