In response to the significant estimation errors in hop period and hop frequency under low signal-to-noise ratio (SNR) conditions, a method for hop period and hop frequency estimation based on maximum entropy binarization of time-frequency maps and a detection and localization (DL)-YOLOv5s model was proposed. Firstly, the maximum entropy thresholding method combined with morphological filtering was utilized to process the time-frequency map, resulting in a clear maximum entropy binarized time-frequency map. Then, the proposed DL-YOLOv5s model was appllied to detect and localize the hop frequency signals within the maximum entropy binarized time-frequency map. By incorporating the ASPP module and BiFPN module, the precision of edge and corner detection for hop frequency signals was enhanced. Additionally, a multi-head self-attention mechanism was introduced to improve the localization accuracy of hop frequency signals by the BOT3 module. Ultimately, the coordinates of the hop frequency signals were obtained, and through the correlation of these coordinates, the estimation of hop period and frequency was completed. The experimental results show that compared to the YOLOv5s model, the proposed DL-YOLOv5s model improves precision (P) by 5%, recall (R) by 2.2%, and the mean average precision (mAP) at 0.5 and mAP at 0.5:0.9 by 5.1% and 4.2% respectively. In comparison to other models such as YOLOv7 and YOLOv8, the proposed DL-YOLOv5s model is smaller in size, making it more suitable for resource-constrained environments like embedded devices commonly used for frequency-hopping signal parameter estimation. Additionally, compared to traditional methods for frequency-hopping signal parameter estimation, the proposed method effectively reduces the estimation errors of hopping period and hopping frequency under low signal-to-noise ratio conditions
