A novel detection framework integrating automatic fluid target annotation and a lightweight YOLOv8n model was proposed, aiming at addressing the challenges of low annotation efficiency and insufficient model adaptability in real-time debris flow monitoring. An open-source debris flow dataset of 8 064 images was built. This dataset was achieved using multi-scale dynamic capture frames, which leverage fluid motion continuity, along with adaptive frame extraction and an improved binary classification model. To enhance the extraction of dynamic debris flow features and improve the irregular boundary localization, the LGSA module and C2f_GhostNetV2 structure were developed. The Shape-IoU loss function was also introduced into the model architecture. Experimental results demonstrated that the proposed YOLOv8-Mudslide model achieved an mAP@0.5 of 86.7%, which was 4.7% higher than that of the baseline model, and the detection speed reached 230.89 frame/s. This case provides reliable technical support for the real-time monitoring of debris flow disasters, and its framework can be further extended to other intelligent detection fields of fluid targets.