Mirror-Yolo: An attention-based instance segmentation and detection
model for mirrors
- URL: http://arxiv.org/abs/2202.08498v1
- Date: Thu, 17 Feb 2022 08:03:48 GMT
- Title: Mirror-Yolo: An attention-based instance segmentation and detection
model for mirrors
- Authors: Fengze Li, Jieming Ma, Zhongbei Tian, Ji Ge, Hai-Ning Liang, Yungang
Zhang and Tianxi Wen
- Abstract summary: YOLOv4 achieves phenomenal results both in object detection accuracy and speed.
Mirror-YOLO is proposed, which mainly targets on mirror detection.
- Score: 7.26389301409471
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Mirrors can degrade the performance of computer vision models, however to
accurately detect mirrors in images remains challenging. YOLOv4 achieves
phenomenal results both in object detection accuracy and speed, nevertheless
the model often fails in detecting mirrors. In this paper, a novel mirror
detection method `Mirror-YOLO' is proposed, which mainly targets on mirror
detection. Based on YOLOv4, the proposed model embeds an attention mechanism
for better feature acquisition, and a hypercolumn-stairstep approach for
feature map fusion. Mirror-YOLO can also produce accurate bounding polygons for
instance segmentation. The effectiveness of our proposed model is demonstrated
by our experiments, compared to the existing mirror detection methods, the
proposed Mirror-YOLO achieves better performance in detection accuracy on the
mirror image dataset.
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