Long-distance tiny face detection based on enhanced YOLOv3 for unmanned system

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.

基于增强型YOLOv3的远程无人脸检测系统

在无人系统中应用的远程小脸检测是一项具有挑战性的工作。由于距离相对较长，检测器无法获得足够的上下文语义信息。.. 收到的较差的细粒度功能会使人脸检测的准确性和鲁棒性降低。为了解决人脸的远程检测问题，我们提出了一种基于YOLOv3算法的无人平台增强网络模型（YOLOv3-C）。在该模型中，我们从特征金字塔网络中引入多尺度特征，并使特征融合以调整输出的预测特征图，从而提高了整个算法对微小目标脸部的敏感性。增强的模型提高了在长距离和高密度人群中小脸检测的准确性。实验评估结果表明，与其他相关检测器相比，拟议的YOLOv3-C在远程细小脸部检测方面具有优越的性能。 （阅读更多）