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Multiscale IoU: A Metric for Evaluation of Salient Object Detection with Fine Structures
![](https://sigport.org/sites/default/files/styles/home/public/3_examples_with_plots.png?itok=1ij_dsss)
- Citation Author(s):
- Submitted by:
- Azim Ahmadzadeh
- Last updated:
- 23 September 2021 - 4:40pm
- Document Type:
- Poster
- Document Year:
- 2021
- Event:
- Presenters:
- Azim Ahmadzadeh
- Paper Code:
- 1802
- Categories:
- Keywords:
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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.