@inproceedings{f6cd3f52abfd44da9bbede521c8c9e3f,
title = "Mirror-Yolo: A Novel Attention Focus, Instance Segmentation and Mirror Detection Model",
abstract = "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.",
keywords = "Object detection, YOLOv4, attention mechanism, mirror bounding polygons, mirror detection",
author = "Fengze Li and Jieming Ma and Zhongbei Tian and Ji Ge and Liang, {Hai Ning} and Yungang Zhang and Tianxi Wen",
note = "Publisher Copyright: {\textcopyright} 2022 IEEE.; 7th International Conference on Frontiers of Signal Processing, ICFSP 2022 ; Conference date: 07-09-2022 Through 09-09-2022",
year = "2022",
doi = "10.1109/ICFSP55781.2022.9925001",
language = "English",
series = "2022 7th International Conference on Frontiers of Signal Processing, ICFSP 2022",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "76--80",
booktitle = "2022 7th International Conference on Frontiers of Signal Processing, ICFSP 2022",
}