An aerial small target detection algorithm that balanced performance and resource consumption was proposed to address the issues of low detection accuracy and large model parameter size in small target detection of aerial images. On the basis of YOLOv8s, an adaptive detail-enhanced module (ADEM) was proposed by reducing the channel dimension and enhancing the focus on the high-frequency features to capture the fine-grained features of small targets while discarding the redundant information. A feature fusion network was optimized based on the PAN-FPN architecture to enhance the attention on shallow features. Multi-scale convolutional kernels were introduced to enhance the focus on the target contextual information, thereby adapting to the small object detection scenario. A parameter-adjustable Nin-IoU was constructed to overcome the limitations of traditional IoU in flexibility and generalization, and this adjustment achieved by introducing adjustable parameters allowed the Nin-IoU to be tailored to different detection tasks. A lightweight detection head was proposed to enhance the integration of multi-scale feature information while reducing redundant information transmission. Experimental results on the VisDrone2019 dataset indicated that the proposed algorithm achieved an mAP_0.5 of 50.3% with only 8.08×10⁶ parameters, representing a 27.4% reduction in parameters and an improvement of 11.5 percentage points in accuracy compared to the YOLOv8s benchmark algorithm. Experimental results on the DOTA and DIOR datasets further demonstrated the strong generalization capabilities of the proposed algorithm.