论文标题
补充几何数据的微小Yolo对象检测
Tiny-YOLO object detection supplemented with geometrical data
论文作者
论文摘要
我们提出了一种借助有关场景几何学的知识来提高检测精度（MAP）的方法：我们假设场景是一个带有对象的平面。我们将注意力集中在自主机器人上，因此，鉴于机器人的尺寸和相机的倾斜角度，可以预测输入框架每个像素的空间比例。通过稍微修改的Yolov3微小，我们证明了按比例通道补充的检测进一步称为S，其表现优于基于标准的RGB检测，并用小型计算开销。
We propose a method of improving detection precision (mAP) with the help of the prior knowledge about the scene geometry: we assume the scene to be a plane with objects placed on it. We focus our attention on autonomous robots, so given the robot's dimensions and the inclination angles of the camera, it is possible to predict the spatial scale for each pixel of the input frame. With slightly modified YOLOv3-tiny we demonstrate that the detection supplemented by the scale channel, further referred as S, outperforms standard RGB-based detection with small computational overhead.
