Ancient tombs， as significant relics that mirror the early social class and its lifestyle， constitute crucial objects of archaeological research. However， numerous ancient tombs remain undiscovered. Traditional archaeological investigations of ancient tombs， especially those conducted in vast and complex environments， are often inefficient. The automatic identification method based on deep learning algorithms has the potential to enhance archaeological detection efficiency and thus plays an important role in remote sensing archaeological investigation.This paper focuses on ancient circular tombs in the Altai region of Xinjiang as the subject of research. We have built the first dataset of ancient circular tombs and conducted a comparative study utilizing four mainstream algorithms， Faster R-CNN， Cascade R-CNN， YOLOv5 and YOLOv7， to detect these tombs. The result reveals that the average precision of YOLOv5 and YOLOv7 exceeds 0.8， with YOLOv7 achieving a peak accuracy of 0.87， showcasing the robustness and adaptability of the YOLO algorithms. Additionally， we have tested the four deep learning algorithms on remote sensing data of varying resolutions， including WorldView2， WorldView3 and GF2 satellites. YOLOv7 emerges as the superior algorithm in terms of both efficiency and accuracy for identifying ancient tombs with blob and ring archaeological markers. This comparative study， not only offers an effective algorithm for the automatic detection of ancient tombs but also provides valuable insights for the automatic detection of other archaeological relics.