# Focaler-IoU: More Focused Intersection over Union Loss

arXiv: https://arxiv.org/abs/2401.10525 · HTML: https://mlsanity.com/paper/2401.10525/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 13 references that are in the map.
- First posted on arXiv 19 Jan 2024.

## Builds on (most cited first, 12 of 12)

- [You Only Look Once: Unified, Real-Time Object Detection](https://mlsanity.com/paper/1506.02640/) (2015)
- [SSD: Single Shot MultiBox Detector](https://mlsanity.com/paper/1512.02325/) (2015)
- [Fast R-CNN](https://mlsanity.com/paper/1504.08083/) (2015)
- [FCOS: Fully Convolutional One-Stage Object Detection](https://mlsanity.com/paper/1904.01355/) (2019)
- [Generalized Intersection over Union: A Metric and A Loss for Bounding Box Regression](https://mlsanity.com/paper/1902.09630/) (2019)
- [CornerNet: Detecting Objects as Paired Keypoints](https://mlsanity.com/paper/1808.01244/) (2018)
- [CenterNet: Keypoint Triplets for Object Detection](https://mlsanity.com/paper/1904.08189/) (2019)
- [Distance-IoU Loss: Faster and Better Learning for Bounding Box Regression](https://mlsanity.com/paper/1911.08287/) (2019)
- [Libra R-CNN: Towards Balanced Learning for Object Detection](https://mlsanity.com/paper/1904.02701/) (2019)
- [UnitBox: An Advanced Object Detection Network](https://mlsanity.com/paper/1608.01471/) (2016)
- [Focal and Efficient IOU Loss for Accurate Bounding Box Regression](https://mlsanity.com/paper/2101.08158/) (2021)
- [SIoU Loss: More Powerful Learning for Bounding Box Regression](https://mlsanity.com/paper/2205.12740/) (2022)

## Built on by (most cited first, 9 of 9)

- [PK-YOLO: Pretrained Knowledge Guided YOLO for Brain Tumor Detection in Multiplanar MRI Slices](https://mlsanity.com/paper/2410.21822/) (2024)
- [A Real-time Concrete Crack Detection and Segmentation Model Based on YOLOv11](https://mlsanity.com/paper/2508.11517/) (2025)
- [SNAT-YOLO: Efficient Cross-Layer Aggregation Network for Edge-Oriented Gangue Detection](https://mlsanity.com/paper/2502.05988/) (2025)
- [YOLO-APD: Enhancing YOLOv8 for Robust Pedestrian Detection on Complex Road Geometries](https://mlsanity.com/paper/2507.05376/) (2025)
- [Inter-Class Relational Loss for Small Object Detection: A Case Study on License Plates](https://mlsanity.com/paper/2508.14343/) (2025)
- [TinyDef-DETR: A Transformer-Based Framework for Defect Detection in Transmission Lines from UAV Imagery](https://mlsanity.com/paper/2509.06035/) (2025)
- [PT-DETR: Small Target Detection Based on Partially-Aware Detail Focus](https://mlsanity.com/paper/2510.26630/) (2025)
- [An Intelligent-Cloud Edge Multimodal Interaction System for Robots](https://mlsanity.com/paper/2607.14675/) (2026)
- [Contour-Guided Spectral Routing for Robust Real-Time Pedestrian Detection](https://mlsanity.com/paper/2609.14383/) (2026)

## Abstract (arXiv)

Bounding box regression plays a crucial role in the field of object detection, and the positioning accuracy of object detection largely depends on the loss function of bounding box regression. Existing researchs improve regression performance by utilizing the geometric relationship between bounding boxes, while ignoring the impact of difficult and easy sample distribution on bounding box regression. In this article, we analyzed the impact of difficult and easy sample distribution on regression results, and then proposed Focaler-IoU, which can improve detector performance in different detection tasks by focusing on different regression samples. Finally, comparative experiments were conducted using existing advanced detectors and regression methods for different detection tasks, and the detection performance was further improved by using the method proposed in this paper.Code is available at urlhttps://github.com/malagoutou/Focaler-IoU.