**5. Conclusions**

In this paper, we investigated and examined various perspectives on the causes of disparity in the detection performance between adults and children in the task of pedestrian detection. As a first experiment, we confirmed that self-training extends the pre-training model and improves the overall detection performance. Then, we found that applying data augmentation to the bounding boxes of children—for whom there is less data available than for adults—significantly improves the detection performance for adults but not children. We also visualized the feature distribution of the bounding boxes using t-SNE and found that there was no apparent difference between adults and children. These results indicate that it is not necessary to consider the bias in the quantity of data in terms of age attributes in pedestrian detection.

On the other hand, when we looked at the size of the bounding boxes in our FA-INRIA, we observed that the distribution was biased toward a smaller size for children than for adults. In addition, we found that changing the input size of the image fed to the detector had a significant impact on detection performance for children. In other words, we concluded that the disparity in the size of the bounding boxes was a major factor in the

disparity in detection performance among age attributes. In the future, focusing on the detection of small bounding boxes will help mitigate the bias between attributes.

**Author Contributions:** Main contribution: S.K., K.W. and R.Y.; supervision: Y.A., A.N. and H.K. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research received no external funding.

**Institutional Review Board Statement:** Not applicable.

**Informed Consent Statement:** Not applicable.

**Data Availability Statement:** Not applicable.

**Conflicts of Interest:** The authors declare no conflict of interest.
