  Remote tiny face detection applied in unmanned system is a challeng-ing work.
The detector cannot obtain sufficient context semantic information due to the
relatively long distance. The received poor fine-grained features make the face
detection less accurate and robust. To solve the problem of long-distance
detection of tiny faces, we propose an enhanced network model (YOLOv3-C) based
on the YOLOv3 algorithm for unmanned platform. In this model, we bring in
multi-scale features from feature pyramid networks and make the features
fu-sion to adjust prediction feature map of the output, which improves the
sensitivity of the entire algorithm for tiny target faces. The enhanced model
improves the accuracy of tiny face detection in the cases of long-distance and
high-density crowds. The experimental evaluation results demonstrated the
superior perfor-mance of the proposed YOLOv3-C in comparison with other
relevant detectors in remote tiny face detection. It is worth mentioning that
our proposed method achieves comparable performance with the state of the art
YOLOv4[1] in the tiny face detection tasks.
