Multiscale IoU: A Metric for Evaluation of Salient Object Detection with
Fine Structures
- URL: http://arxiv.org/abs/2105.14572v1
- Date: Sun, 30 May 2021 15:31:42 GMT
- Title: Multiscale IoU: A Metric for Evaluation of Salient Object Detection with
Fine Structures
- Authors: Azim Ahmadzadeh, Dustin J. Kempton, Yang Chen, Rafal A. Angryk
- Abstract summary: General-purpose object-detection algorithms often dismiss the fine structure of detected objects.
We present a new metric that is a marriage of a popular evaluation metric, namely Intersection over Union (IoU), and a geometrical concept, called fractal dimension.
We show that MIoU is indeed sensitive to the fine boundary structures which are completely overlooked by IoU and f1-score.
- Score: 5.098461305284216
- License: http://creativecommons.org/licenses/by-nc-sa/4.0/
- Abstract: General-purpose object-detection algorithms often dismiss the fine structure
of detected objects. This can be traced back to how their proposed regions are
evaluated. Our goal is to renegotiate the trade-off between the generality of
these algorithms and their coarse detections. In this work, we present a new
metric that is a marriage of a popular evaluation metric, namely Intersection
over Union (IoU), and a geometrical concept, called fractal dimension. We
propose Multiscale IoU (MIoU) which allows comparison between the detected and
ground-truth regions at multiple resolution levels. Through several
reproducible examples, we show that MIoU is indeed sensitive to the fine
boundary structures which are completely overlooked by IoU and f1-score. We
further examine the overall reliability of MIoU by comparing its distribution
with that of IoU on synthetic and real-world datasets of objects. We intend
this work to re-initiate exploration of new evaluation methods for
object-detection algorithms.
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