Abstract: ‍ ‍Small target detection plays a crucial role in applications such as low-altitude aircraft management， environmental monitoring， and border security， serving as a key technology for ensuring low-altitude airspace safety and promoting the development of the low-altitude economy. However， conventional radar target detection algorithms often poorly perform when dealing with “low， slow， and small” targets due to challenges such as limited target size， low signal-to-noise ratio （SNR）， and interference from background clutter. This study proposes feeding the radar Range-Doppler （RD） plane into an improved YOLOv8 model and validating the approach using real radar data， resulting in enhanced small target detection performance to address these issues. First， a holographic staring radar is used to collect real-world data， followed by data annotation and dataset preparation. In terms of model design， this paper introduces the Efficient Channel Attention （ECA） mechanism， which effectively captures dependencies between channels by replacing traditional fully connected layers with one-dimensional convolutions without adding extra parameters. This enhancement improves the ability to select relevant features of the network across different channels， thereby enabling more accurate small target detection. Additionally， a dedicated small target detection layer is incorporated into the model. This layer adjusts the resolution of feature maps， allowing the model to extract and recognize features from even smaller targets， addressing the shortcomings of conventional detection networks in handling small targets. Furthermore， multiple models are compared in the experimental section. The results show that the proposed improved model outperforms other models across multiple evaluation metrics. Compared to the baseline YOLOv8n， the improved model achieves a 2.3% increase in precision，a 1.9% increase in mAP@0.5， and a 3.5% increase in mAP@0.5-0.95. Overall， the proposed model demonstrates superior detection performance in terms of precision， recall， and mAP@0.5， validating its effectiveness for radar-based small target detection.