论文标题
镜像 - Yolo：新颖的注意力集中，实例分割和镜像检测模型
Mirror-Yolo: A Novel Attention Focus, Instance Segmentation and Mirror Detection Model
论文作者
论文摘要
镜子可以降低计算机视觉模型的性能，但是检测它们的研究处于初步阶段。 Yolov4在对象检测准确性和速度方面取得了惊人的结果，但仍无法检测镜子。因此，我们提出了针对镜像检测的Mirror-Yolo，其中包含用于特征采集的新型注意力重点机制，一种更好的融合特征图和镜面边界多边形的方法，例如分割。与现有的镜像检测网络和YOLO系列相比，我们提出的网络在我们提出的镜像数据集和另一个最先进的镜像数据集上的平均准确性达到了卓越的性能，这证明了镜像Yolo的有效性和有效性。
Mirrors can degrade the performance of computer vision models, but research into detecting them is in the preliminary phase. YOLOv4 achieves phenomenal results in terms of object detection accuracy and speed, but it still fails in detecting mirrors. Thus, we propose Mirror-YOLO, which targets mirror detection, containing a novel attention focus mechanism for features acquisition, a hypercolumn-stairstep approach to better fusion the feature maps, and the mirror bounding polygons for instance segmentation. Compared to the existing mirror detection networks and YOLO series, our proposed network achieves superior performance in average accuracy on our proposed mirror dataset and another state-of-art mirror dataset, which demonstrates the validity and effectiveness of Mirror-YOLO.
