This paper proposes a new bidirectional weighted multiscale dynamic approach, the BiEO-YOLOv8s algorithm, to enhance the detection of small targets in aerial images. It effectively addresses challenges such as complex backgrounds, large-scale variations, and dense targets. First, we design a new ODE module to replace certain C2f modules, enabling the accurate, quick, and multiangle location of target features. Then, we develop a bidirectional weighted multiscale dynamic neck network structure (BiEO-Neck) to achieve deep fusion of shallow and deep features. Second, adding a small object detection head further enhances feature extraction capability. Finally, the generalized intersection union ratio boundary loss function is used to replace the original boundary loss function, thereby enhancing the regression performance of the bounding box. Experiments conducted on the VisDrone dataset demonstrat that as compared to the base model YOLOv8s, the proposed model achieved a 6.1 percentage points improvement in mean average precision, with a detection speed of only 4.9 ms. This performance surpasses that of other mainstream models. The algorithm effectiveness and adaptability are further confirmed through universality testing on the IRTarget dataset. The proposed algorithm can efficiently complete target detection tasks in of unmanned aerial vehicle aerial images.