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arXiv · 2105.14572

Multiscale IoU: A Metric for Evaluation of Salient Object Detection with Fine Structures

Azim Ahmadzadeh·Dustin J. Kempton·Yang Chen·Rafal A. Angryk
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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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Azim Ahmadzadeh, Dustin J. Kempton, Yang Chen, Rafal A. Angryk. 2021-05-30. Multiscale IoU: A Metric for Evaluation of Salient Object Detection with Fine Structures. https://arxiv.org/abs/2105.14572

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