Truncation is a phenomenon where an object is partially outside the image boundary and cannot be fully displayed due to its position at the image edge. It is often marked in dataset annotations. Learning from truncated samples can improve the model's detection performance for edge and incomplete objects, adapting to the characteristics of actual captured images.
