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SIoU Loss: More Powerful Learning for Bounding Box Regression

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arxiv 2205.12740 v1 pith:5FH6HBGW submitted 2022-05-25 cs.CV cs.AI

Zhora Gevorgyan This is my paper · ORCID
classification cs.CVcs.AI
keywords lossfunctionpredictedregressionsiouboundingconventionaldesired
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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.

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Cited by 13 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. An Uncertainty-aware DETR Enhancement Framework for Object Detection

    cs.CV 2025-07 conditional novelty 6.0 of 10

    Adding a Gaussian-box Gromov-Wasserstein loss and Bayes-risk-based refinement to DETR detectors improves their AP on COCO and leukocyte datasets while producing localization uncertainty estimates.

  2. Towards Continuous Home Cage Monitoring: An Evaluation of Tracking and Identification Strategies for Laboratory Mice

    cs.CV 2025-07 conditional novelty 6.0 of 10

    A real-time mouse tracking and ear-tag identity pipeline reports 95.28% identification accuracy and fewer ID switches than SLEAP and DeepLabCut on a 100-minute home-cage dataset.

  3. Mitigating Context Bias in Domain Adaptation for Object Detection using Mask Pooling

    cs.CV 2025-05 conditional novelty 6.0 of 10

    Mask Pooling, which pools foreground and background separately using ground-truth masks, improves object-detection robustness on domain-shift benchmarks but requires masks at inference and lacks a rigorous causal derivation.

  4. UAV-DETR: Efficient End-to-End Object Detection for Unmanned Aerial Vehicle Imagery

    cs.CV 2025-01 conditional novelty 6.0 of 10

    A real-time end-to-end UAV object detector that combines spatial and frequency-domain features, gaining 3.1 AP over RT-DETR on VisDrone.

  5. RT-VLM: Re-Thinking Vision Language Model with 4-Clues for Real-World Object Recognition Robustness

    cs.CV 2025-09 conditional novelty 5.0 of 10

    Fine-tuning a vision-language model on a synthetic 4-clue dataset and adding a self-critique inference loop improves robustness to domain shifts in object recognition.

  6. Efficient Vision-based Vehicle Speed Estimation

    cs.CV 2025-05 conditional novelty 5.0 of 10

    Replacing the detector in a vanishing-point-based speed estimation pipeline with YOLOv6 plus post-training quantization yields comparable or better speed accuracy at substantially higher frame rates on BrnoCompSpeed.

  7. YOLOv14: Adaptive Real-Time Object Detection for Diverse Imaging Conditions

    cs.CV 2026-08 reject novelty 4.0 of 10

    YOLOv14 reports a real-time detector with 49.1 COCO mAP and cross-domain gains, but its headline game-character gain is measured on a benchmark synthesized from its own training augmentation.

  8. An Intelligent-Cloud Edge Multimodal Interaction System for Robots

    cs.RO 2026-07 conditional novelty 4.0 of 10

    A cloud-edge robot system combined a CBAM/DIoU-enhanced YOLO11n gesture detector with LLM/VLM agents, reporting 95–98.9% precision and 82–95% task success on small, validation-based evaluations.

  9. VR-YOLO: Enhancing PCB Defect Detection with Viewpoint Robustness Based on YOLO

    cs.CV 2025-06 conditional novelty 4.0 of 10

    VR-YOLO, a YOLOv8 variant with SIoU loss, CBAM attention, and augmented training data, reports mAP 98.9% on the PKU PCB dataset and 94.7% on sheared/rotated test images, but the viewpoint evaluation is in-distribution...

  10. DARTer: Dynamic Adaptive Representation Tracker for Nighttime UAV Tracking

    cs.CV 2025-05 conditional novelty 4.0 of 10

    DARTer combines a Dynamic Feature Blender and a Dynamic Feature Activator to improve nighttime UAV tracking accuracy and efficiency, outperforming prior trackers on five benchmarks.

  11. Foreign-Object Detection in High-Voltage Transmission Line Based on Improved YOLOv8m

    cs.CV 2025-02 conditional novelty 3.0 of 10

    An improved YOLOv8m (GAM attention, SPPCSPC, Focal-EIoU) reports higher mAP and recall for detecting trash, twigs, nests, kites, birds, and balloons on Yunnan power lines.

  12. RS-YOLOX: A High Precision Detector for Object Detection in Satellite Remote Sensing Images

    cs.CV 2025-02 reject novelty 2.0 of 10

    RS-YOLOX combines ECA, ASFF, Varifocal Loss, and SAHI with YOLOX and reports mAP gains of about 5 points on three remote sensing datasets, but the evaluation protocol may leak augmented images into the test set.

  13. What is YOLOv6? A Deep Insight into the Object Detection Model

    cs.CV 2024-12 unverdicted novelty 1.0 of 10

    A review-style paper that restates YOLOv6's architecture and benchmark tables from the YOLOv6 paper without adding new experiments or analysis.

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