In order to address the problem of missed and false detections caused by different object scales， densities， unclear details and especially numerous small objects in drone aerial images， a novel object detection method based on Adaptive Slicing Aided Inference （ASAI） is proposed. Firstly， an aerial image is input into an object detection network for initial inference. With the initial inference results， a window scoring mechanism is designed to locate the ambiguous targets in the input image， and the effective image regions are selected automatically for slicing to adapt to objects with different scales and densities. Then， the sliced images are sent to the object detection network for secondary inference. Finally， the two inference results are processed by an improved non-maximum suppression （NMS） algorithm to obtain the final detection result. Experimental results on typical datasets of VisDrone2019 and AI-TOD indicate that the proposed method improves the mAP metrics of typical lightweight object detection models including YOLOv7-tiny， YOLOv8n， YOLOv8s and YOLOv9-C， effectively improving the performance of object detection for aerial images.

small object detection;adaptive slicing aided inference;ambiguous target localization;Non-maximum suppression