Abstract To achieve automated counting of drill pipes during gas extraction in coal mines, this study proposes an intelligent counting method based on an improved YOLOv11 model and Savitzky-Golay (SG) smoothing. First, images of underground gas extraction drilling operations were captured, and a dataset was constructed. Subsequently, improvement strategies for the YOLOv11 object detection model were developed to meet the requirements for rapid drill pipe counting. Ablation experiments demonstrate that the proposed improvements enhance YOLOv11’s detection accuracy. The improved YOLOv11 achieves precision, recall, mAP50, and mAP50-95 scores of 94.7%, 95.7%, 96.6%, and 65.8%, respectively, surpassing the performance of YOLOv8, YOLOX, Faster R-CNN, and CenterNet. The trained YOLOv11 model was then applied to detect drill pipes in test videos, and SG smoothing was applied to the bounding box area curves. Finally, the smoothed area curves were used to count the drill pipes, with the test video confirming a total of nine drill pipes, consistent with the actual count. The results demonstrate that the proposed method effectively mitigates the impact of complex underground environments and achieves intelligent and accurate drill pipe counting, providing technical support for standardized gas extraction in coal mines.