Mirror-Yolo: A Novel Attention Focus, Instance Segmentation and Mirror Detection Model
This addresses mirror detection, a preliminary research area, for computer vision applications, but it appears incremental as it builds on YOLOv4 with specific enhancements.
The paper tackles the problem of mirror detection in computer vision, which degrades model performance, by proposing Mirror-YOLO, a model that achieves superior average accuracy on mirror datasets compared to existing networks and YOLO series.
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.