Abstract: Aiming to address the issues with current gangue mixed ratio detection methods in fully mechanized caving face based on deep learning, such as low accuracy of coal gangue identification, poor segmentation performance, large model parameters and computation load, and the inability to achieve real-time detection of gangue mixed ratio under complex conditions such as low lighting, high dust, and coal and angue stacking, the paper proposed a gangue mixed ratio detection method based on the GSL-YOLO model. The GSL-YOLO model introduced the following improvements to the YOLOv8-seg model: the incorporation of a global attention mechanism (GAM) in the backbone network to enhance feature extraction by reducing information dispersion and amplifying global interaction representation; the use of a spatial pyramid pooling with efficient local aggregation network (SPPELAN) module to improve detection performance for targets of varying sizes; and the adoption of a lightweight asymmetric dual-head (LADH) to reduce training difficulty while increasing inference speed. Additionally, a gangue mixed ratio calculation method based on category segmentation masks was proposed, which calculated the ratio of the pixel area of gangue to the total pixel area in the segmentation mask of coal flow images, serving as the instantaneous gangue mixed ratio. Experimental results showed that: ① The GSL-YOLO model achieved an mAP@0.5∶0.95 of 96.1%, which was 0.8% higher than the YOLOv8-seg model. ② The GSL-YOLO model had 2.9×10⁶ parameters, 11.4×10⁹ floating-point operations, and a model weight of 6.0 MiB, representing reductions of 12.1%, 5.8%, and 11.8% respectively compared to the YOLOv8-seg model, achieving model lightweighting. ③ The GSL-YOLO model achieved a frame rate of 12 frames per second on the test set, essentially meeting the requirements for real-time detection. ④ Compared with the YOLO series models, the GSL-YOLO model had the best segmentation effect, the highest detection accuracy, fewer parameters and computation load, and the best overall performance. ⑤ Based on three frames of images captured from the coal flow on the rear scraper conveyor of the fully mechanized caving face, the instantaneous gangue mixed ratio was calculated, and the results showed that the proposed method successfully realized real-time calculation of the gangue mixed ratio in fully mechanized caving face.