In aerial images of prairies, rat holes occupy an extremely small proportion, thus belonging to the small-target detection task. Rat holes blend so well with the surrounding environment that information like their texture and boundaries is often obscured by the complex background. As a result, rat-hole detection can be classified as small and weak target detection. Additionally, the frequently - occurring ground shadows in the prairie environment interfere with rat - hole detection. This interference tends to generate pre - selected bounding boxes with high Intersection over Union (IoU) values during the small and weak target detection process, leading to a high false - detection rate. In this paper, we distinguish shadows by exploring the depth - of - field characteristics of rat holes. We enhance the anti - interference ability of positive samples by fusing the depth - information channel with the RGB channel. Moreover, we improve the backbone network of Faster R - CNN to further extract detailed features. We selected rat holes in two prairie regions of Inner Mongolia with frequent rodent - infestation as the research subjects and constructed an aerial - photography dataset of prairie rat holes named RatHoles. Through in - depth analysis of the target features in the rat - hole dataset, we proposed a small and weak target detection model for prairie rat holes, DEFA, which integrates depth - estimation information. The Average Precision (AP) value of DEFA is 4.2% higher than that of the DNTR model, a small and weak target detection model proposed in 2024.