# Weighted Intersection over Union (wIoU) for Evaluating Image Segmentation

arXiv: https://arxiv.org/abs/2107.09858 · HTML: https://mlsanity.com/paper/2107.09858/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 32 references that are in the map.
- First posted on arXiv 21 Jul 2021.

## Builds on (most cited first, 13 of 13)

- [Fully Convolutional Networks for Semantic Segmentation](https://mlsanity.com/paper/1411.4038/) (2014)
- [The Cityscapes Dataset for Semantic Urban Scene Understanding](https://mlsanity.com/paper/1604.01685/) (2016)
- [Rich feature hierarchies for accurate object detection and semantic segmentation](https://mlsanity.com/paper/1311.2524/) (2013)
- [Gated-SCNN: Gated Shape CNNs for Semantic Segmentation](https://mlsanity.com/paper/1907.05740/) (2019)
- [Improving Semantic Segmentation via Video Propagation and Label Relaxation](https://mlsanity.com/paper/1812.01593/) (2018)
- [Augmented Reality Meets Computer Vision : Efficient Data Generation for Urban Driving Scenes](https://mlsanity.com/paper/1708.01566/) (2017)
- [A Multi-World Approach to Question Answering about Real-World Scenes based on Uncertain Input](https://mlsanity.com/paper/1410.0210/) (2014)
- [SegFix: Model-Agnostic Boundary Refinement for Segmentation](https://mlsanity.com/paper/2007.04269/) (2020)
- [CNN-based Segmentation of Medical Imaging Data](https://mlsanity.com/paper/1701.03056/) (2017)
- [Restricted Deformable Convolution based Road Scene Semantic Segmentation Using Surround View Cameras](https://mlsanity.com/paper/1801.00708/) (2018)
- [Classification With an Edge: Improving Semantic Image Segmentation with Boundary Detection](https://mlsanity.com/paper/1612.01337/) (2016)
- [Semantic Segmentation with Boundary Neural Fields](https://mlsanity.com/paper/1511.02674/) (2015)
- [A Survey of Semantic Segmentation](https://mlsanity.com/paper/1602.06541/) (2016)

## Built on by (most cited first, 11 of 11)

- [Fostc3net:A Lightweight YOLOv5 Based On the Network Structure Optimization](https://mlsanity.com/paper/2403.13703/) (2024)
- [TransUNet-GradCAM: A Hybrid Transformer-U-Net with Self-Attention and Explainable Visualizations for Foot Ulcer Segmentation](https://mlsanity.com/paper/2508.03758/) (2025)
- [Context Determines Optimal Architecture in Materials Segmentation](https://mlsanity.com/paper/2602.04154/) (2026)
- [Robust Multispectral Semantic Segmentation under Missing or Full Modalities via Structured Latent Projection](https://mlsanity.com/paper/2604.15856/) (2026)
- [Revisiting Change Detection Methods for their Application to Serac Fall Time-Lapse Monitoring](https://mlsanity.com/paper/2605.28100/) (2026)
- [Precision in Building Extraction: Comparing Shallow and Deep Models using LiDAR Data](https://mlsanity.com/paper/2309.12027/) (2023)
- [Improved Segmentation of Polyps and Visual Explainability Analysis](https://mlsanity.com/paper/2509.18159/) (2025)
- [WarNav: An Autonomous Driving Benchmark for Segmentation of Navigable Zones in War Scenes](https://mlsanity.com/paper/2511.15429/) (2025)
- [PanopMamba: Vision State Space Modeling for Nuclei Panoptic Segmentation](https://mlsanity.com/paper/2601.16631/) (2026)
- [Detecting Pen-In-Air States from Video: A Proof-of-Concept Toward Complementary Handwriting Analysis](https://mlsanity.com/paper/2606.02342/) (2026)
- [A Novel Method to Evaluate Models on Unreliable, Noisy and Inconsistent Labels: Adaptive Resolution Label Aggregation (ARLA)](https://mlsanity.com/paper/2607.11214/) (2026)

## Abstract (arXiv)

In recent years, many semantic segmentation methods have been proposed to predict label of pixels in the scene. In general, we measure area prediction errors or boundary prediction errors for comparing methods. However, there is no intuitive evaluation metric that evaluates both aspects. In this work, we propose a new evaluation measure called weighted Intersection over Union (wIoU) for semantic segmentation. First, it builds a weight map generated from a boundary distance map, allowing weighted evaluation for each pixel based on a boundary importance factor. The proposed wIoU can evaluate both contour and region by setting a boundary importance factor. We validated the effectiveness of wIoU on a dataset of 33 scenes and demonstrated its flexibility. Using the proposed metric, we expect more flexible and intuitive evaluation in semantic segmentation field are possible.