You will be shown an excerpt from a computer science scientific research paper, followed by a question about the paper. Please answer the question. Do not include any text in your response other than the answer.

Context: We perform the same design one more time to predict boxes for the final scale. Thus our predictions for the 3rd scale benefit from all the prior computation as well as fine-grained features from early on in the network. In the past YOLO struggled with small objects. However, now we see a reversal in that trend. With the new multi-scale predictions we see YOLOv3 has relatively high AP{}_{S} performance. However, it has comparatively worse performance on medium and larger size objects. More investigation is needed to get to the bottom of this.

Question: In paper authors make the predictions at three different scales, but what is advantage of making object detections at different scales?
By using multi-scaled prediction, YOLOv3 has improved performance for small objects. Also, the subsequent scales benefit from previous scales and the previous features from earlier layers.