Foreign object detection on underground coal mine conveyor belts is essential for ensuring production safety, yet challenges such as uneven illumination, dust interference, and motion blur significantly affect detection accuracy and real-time performance. To address these issues, this paper proposes a lightweight detection model, CBFOD-YOLO, based on an improved YOLOv11 architecture. The model incorporates a C3K2_PConv module, which embeds partial convolution (PConv) into the C3K2 structure to expand the receptive field while reducing parameters and computational cost, thereby improving feature extraction efficiency. In addition, a Content-Guided Attention (CGA) mechanism is introduced to generate channel-specific spatial importance maps, enhancing key feature representation and suppressing background noise. Experimental results on a public coal mine foreign object dataset demonstrate that CBFOD-YOLO achieves 90.4% mAP@50, 88.7% precision, and 85.5% recall with only 2.37M parameters and an inference speed of 379.2 FPS, outperforming mainstream models such as YOLOv5, YOLOv8, and YOLOv9t. These results confirm that CBFOD-YOLO strikes an effective balance between accuracy, efficiency, and lightweight design, providing valuable insights for foreign object detection in coal mines and the development of efficient object detection models.