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

arXiv: https://arxiv.org/abs/2105.14572 · HTML: https://mlsanity.com/paper/2105.14572/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 26 references that are in the map.
- First posted on arXiv 30 May 2021.

## Builds on (most cited first, 11 of 11)

- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [ImageNet Large Scale Visual Recognition Challenge](https://mlsanity.com/paper/1409.0575/) (2014)
- [Mask R-CNN](https://mlsanity.com/paper/1703.06870/) (2017)
- [Show, Attend and Tell: Neural Image Caption Generation with Visual Attention](https://mlsanity.com/paper/1502.03044/) (2015)
- [Deep Visual-Semantic Alignments for Generating Image Descriptions](https://mlsanity.com/paper/1412.2306/) (2014)
- [Object Detection in 20 Years: A Survey](https://mlsanity.com/paper/1905.05055/) (2019)
- [Object Detection with Deep Learning: A Review](https://mlsanity.com/paper/1807.05511/) (2018)
- [DeblurGAN: Blind Motion Deblurring Using Conditional Adversarial Networks](https://mlsanity.com/paper/1711.07064/) (2017)
- [The History Began from AlexNet: A Comprehensive Survey on Deep Learning Approaches](https://mlsanity.com/paper/1803.01164/) (2018)
- [Deep Fruit Detection in Orchards](https://mlsanity.com/paper/1610.03677/) (2016)
- [Toward Filament Segmentation Using Deep Neural Networks](https://mlsanity.com/paper/1912.02743/) (2019)

## Built on by (most cited first, 5 of 5)

- [Occ-LLM: Enhancing Autonomous Driving with Occupancy-Based Large Language Models](https://mlsanity.com/paper/2502.06419/) (2025)
- [Multiscale Dubuc: A New Similarity Measure for Time Series](https://mlsanity.com/paper/2411.10418/) (2024)
- [Beyond Pixel Overlap: A Framework for Decomposing Segmentation Evaluation Metrics](https://mlsanity.com/paper/2607.00886/) (2026)
- [A Guide for Manual Annotation of Scientific Imagery: How to Prepare for Large Projects](https://mlsanity.com/paper/2508.14801/) (2025)
- [EdgeAttNet: Towards Barb-Aware Filament Segmentation](https://mlsanity.com/paper/2509.02964/) (2025)

## Abstract (arXiv)

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.