MPDIoU: A Loss for Efficient and Accurate Bounding Box Regression
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Abstract
Bounding box regression (BBR) has been widely used in object detection and instance segmentation, which is an important step in object localization. However, most of the existing loss functions for bounding box regression cannot be optimized when the predicted box has the same aspect ratio as the groundtruth box, but the width and height values are exactly different. In order to tackle the issues mentioned above, we fully explore the geometric features of horizontal rectangle and propose a novel bounding box similarity comparison metric MPDIoU based on minimum point distance, which contains all of the relevant factors considered in the existing loss functions, namely overlapping or non-overlapping area,…
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Topics
Keywords
- Minimum bounding box
- Bounding overwatch
- Pascal (unit)
- Segmentation
- Rectangle
- Regression
- Computer science
- Algorithm
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