# SIoU Loss: More Powerful Learning for Bounding Box Regression

arXiv: https://arxiv.org/abs/2205.12740 · HTML: https://mlsanity.com/paper/2205.12740/

## Key facts

- Cited by 49 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 7 references that are in the map.
- First posted on arXiv 25 May 2022.

## Builds on (most cited first, 7 of 7)

- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [YOLOv3: An Incremental Improvement](https://mlsanity.com/paper/1804.02767/) (2018)
- [YOLOv4: Optimal Speed and Accuracy of Object Detection](https://mlsanity.com/paper/2004.10934/) (2020)
- [Generalized Intersection over Union: A Metric and A Loss for Bounding Box Regression](https://mlsanity.com/paper/1902.09630/) (2019)
- [EfficientDet: Scalable and Efficient Object Detection](https://mlsanity.com/paper/1911.09070/) (2019)
- [Distance-IoU Loss: Faster and Better Learning for Bounding Box Regression](https://mlsanity.com/paper/1911.08287/) (2019)
- [Enhancing Geometric Factors in Model Learning and Inference for Object Detection and Instance Segmentation](https://mlsanity.com/paper/2005.03572/) (2020)

## Built on by (most cited first, 30 of 49)

- [YOLOv6: A Single-Stage Object Detection Framework for Industrial Applications](https://mlsanity.com/paper/2209.02976/) (2022)
- [A Comprehensive Review of YOLO Architectures in Computer Vision: From YOLOv1 to YOLOv8 and YOLO-NAS](https://mlsanity.com/paper/2304.00501/) (2023)
- [Wise-IoU: Bounding Box Regression Loss with Dynamic Focusing Mechanism](https://mlsanity.com/paper/2301.10051/) (2023)
- [UAV-DETR: Efficient End-to-End Object Detection for Unmanned Aerial Vehicle Imagery](https://mlsanity.com/paper/2501.01855/) (2025)
- [Inner-IoU: More Effective Intersection over Union Loss with Auxiliary Bounding Box](https://mlsanity.com/paper/2311.02877/) (2023)
- [Focaler-IoU: More Focused Intersection over Union Loss](https://mlsanity.com/paper/2401.10525/) (2024)
- [Lightweight Full-Convolutional Siamese Tracker](https://mlsanity.com/paper/2310.05392/) (2023)
- [BGF-YOLO: Enhanced YOLOv8 with Multiscale Attentional Feature Fusion for Brain Tumor Detection](https://mlsanity.com/paper/2309.12585/) (2023)
- [ALSS-YOLO: An Adaptive Lightweight Channel Split and Shuffling Network for TIR Wildlife Detection in UAV Imagery](https://mlsanity.com/paper/2409.06259/) (2024)
- [UDTIRI: An Online Open-Source Intelligent Road Inspection Benchmark Suite](https://mlsanity.com/paper/2304.08842/) (2023)
- [Semi-Supervised Object Detection: A Survey on Progress from CNN to Transformer](https://mlsanity.com/paper/2407.08460/) (2024)
- [Edge AI-Enabled Chicken Health Detection Based on Enhanced FCOS-Lite and Knowledge Distillation](https://mlsanity.com/paper/2407.09562/) (2024)
- [Student Classroom Behavior Detection based on Improved YOLOv7](https://mlsanity.com/paper/2306.03318/) (2023)
- [Shape-IoU: More Accurate Metric considering Bounding Box Shape and Scale](https://mlsanity.com/paper/2312.17663/) (2023)
- [LSM-YOLO: A Compact and Effective ROI Detector for Medical Detection](https://mlsanity.com/paper/2408.14087/) (2024)
- [SO-DETR: Leveraging Dual-Domain Features and Knowledge Distillation for Small Object Detection](https://mlsanity.com/paper/2504.11470/) (2025)
- [DARTer: Dynamic Adaptive Representation Tracker for Nighttime UAV Tracking](https://mlsanity.com/paper/2505.00752/) (2025)
- [RS-YOLOX: A High Precision Detector for Object Detection in Satellite Remote Sensing Images](https://mlsanity.com/paper/2502.02850/) (2025)
- [FARTrack: Fast Autoregressive Visual Tracking with High Performance](https://mlsanity.com/paper/2602.03214/) (2026)
- [Intersection over Union with smoothing for bounding box regression](https://mlsanity.com/paper/2303.15067/) (2023)
- [Unified-IoU: For High-Quality Object Detection](https://mlsanity.com/paper/2408.06636/) (2024)
- [EMDFNet: Efficient Multi-scale and Diverse Feature Network for Traffic Sign Detection](https://mlsanity.com/paper/2408.14189/) (2024)
- [PK-YOLO: Pretrained Knowledge Guided YOLO for Brain Tumor Detection in Multiplanar MRI Slices](https://mlsanity.com/paper/2410.21822/) (2024)
- [LAM-YOLO: Drones-based Small Object Detection on Lighting-Occlusion Attention Mechanism YOLO](https://mlsanity.com/paper/2411.00485/) (2024)
- [What is YOLOv6? A Deep Insight into the Object Detection Model](https://mlsanity.com/paper/2412.13006/) (2024)
- [InterpIoU: Rethinking Bounding Box Regression with Interpolation-Based IoU Optimization](https://mlsanity.com/paper/2507.12420/) (2025)
- [COTONET: A custom cotton detection algorithm based on YOLO11 for stage of growth cotton boll detection](https://mlsanity.com/paper/2603.11717/) (2026)
- [Light-YOLOv5: A Lightweight Algorithm for Improved YOLOv5 in Complex Fire Scenarios](https://mlsanity.com/paper/2208.13422/) (2022)
- [MFMAN-YOLO: A Method for Detecting Pole-like Obstacles in Complex Environment](https://mlsanity.com/paper/2307.12548/) (2023)
- [Joint-YODNet: A Light-weight Object Detector for UAVs to Achieve Above 100fps](https://mlsanity.com/paper/2309.15782/) (2023)

## Abstract (arXiv)

The effectiveness of Object Detection, one of the central problems in computer vision tasks, highly depends on the definition of the loss function - a measure of how accurately your ML model can predict the expected outcome. Conventional object detection loss functions depend on aggregation of metrics of bounding box regression such as the distance, overlap area and aspect ratio of the predicted and ground truth boxes (i.e. GIoU, CIoU, ICIoU etc). However, none of the methods proposed and used to date considers the direction of the mismatch between the desired ground box and the predicted, "experimental" box. This shortage results in slower and less effective convergence as the predicted box can "wander around" during the training process and eventually end up producing a worse model. In this paper a new loss function SIoU was suggested, where penalty metrics were redefined considering the angle of the vector between the desired regression. Applied to conventional Neural Networks and datasets it is shown that SIoU improves both the speed of training and the accuracy of the inference. The effectiveness of the proposed loss function was revealed in a number of simulations and tests.