论文标题
基于增强的yolov3的长距离触发检测，无人系统
Long-distance tiny face detection based on enhanced YOLOv3 for unmanned system
论文作者
论文摘要
在无人系统中应用的远程微型面部检测是一项挑战的工作。由于相对较长的距离，检测器无法获得足够的上下文语义信息。收到的不良细粒特征使面部检测降低了准确和稳健。为了解决对微小面孔的长距离检测的问题，我们提出了一个基于无人平台的Yolov3算法的增强网络模型（Yolov3-C）。在此模型中，我们从特征金字塔网络中引入多尺度功能，并制作功能fu-sion来调整输出的预测特征图，从而提高了整个算法对微小目标面的灵敏度。在长距离和高密度人群的情况下，增强的模型提高了小面检测的准确性。实验评估结果表明，与远程微型面部检测中的其他相关检测器相比，所提出的Yolov3-C的出色表现。值得一提的是，我们提出的方法在微小的面部检测任务中与最先进的Yolov4 [1]达到了可比的性能。
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.
