Aiming at the small target scale, complex background, and serious leakage and misdetection of aerial images, an aerial small target detection algorithm based on location awareness and cross-layer feature fusion, DC-YOLOv8s, is proposed.DC-YOLOv8s adds a new small target detection layer, which enhances the sensitivity to the small target scale and improves the detection accuracy. In order to reduce the loss of feature information, a cross-layer feature fusion module is designed to fully fuse the small target shallow semantic information and deep semantic information to enrich the feature representation. Improve the C2f structure, combined with variability convolution to design a sensory field attention module based on position-aware incorporation of residuals, adapting to the changes in the shape of aerial small targets, quickly extracting sensory field features, and reducing the rate of leakage detection and false detection. Finally, the dynamic detection head based on the attention mechanism is used to improve the localization performance of small targets in complex scenes in terms of scale perception, spatial perception, and task perception. The experiments show that on VisDrone2019 dataset, DC-YOLOv8s improves 7.2%, 7.5%, and 9.1% on P, R, and mAP respectively compared to YOLOv8s, which significantly improves the performance of small target detection, and the FPS is 71 frames, which meets the realtime requirement. Experimental verification of model generalizability is carried out on VOC2007+2012, and the effect is better than other classical algorithms.