mlsanityData to 13 Oct 2026

MPDIoU: A Loss for Efficient and Accurate Bounding Box Regression

arXiv 2307.07662 ↗First posted 14 Jul 2023cs.CV cs.AIcited by 10

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Abstract (arXiv)

Bounding box regression (BBR) has been widely used in object detection and instance segmentation, which is an important step in object localization. However, most of the existing loss functions for bounding box regression cannot be optimized when the predicted box has the same aspect ratio as the groundtruth box, but the width and height values are exactly different. In order to tackle the issues mentioned above, we fully explore the geometric features of horizontal rectangle and propose a novel bounding box similarity comparison metric MPDIoU based on minimum point distance, which contains all of the relevant factors considered in the existing loss functions, namely overlapping or non-overlapping area, central points distance, and deviation of width and height, while simplifying the calculation process. On this basis, we propose a bounding box regression loss function based on MPDIoU, called LMPDIoU . Experimental results show that the MPDIoU loss function is applied to state-of-the-art instance segmentation (e.g., YOLACT) and object detection (e.g., YOLOv7) model trained on PASCAL VOC, MS COCO, and IIIT5k outperforms existing loss functions.

Key facts. Cited by 10 papers in the map (data through 13 Oct 2026). Builds on 42 of its 53 references that are in the map. First posted on arXiv 14 Jul 2023.

Builds on

42 of 53 references.

References · 42

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30 more
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Built on by

Cited by year (2026 to 13 Oct)

Citing papers per year: 2024: 3; 2025: 4; 2026: 3.

10 papers.

  1. 2024SOD-YOLOv8 -- Enhancing YOLOv8 for Small Object Detection in Traffic Scenescited by 3
  2. 2024PK-YOLO: Pretrained Knowledge Guided YOLO for Brain Tumor Detection in Multiplanar MRI Slicescited by 1
  3. 2025HGO-YOLO: Advancing Anomaly Behavior Detection with Hierarchical Features and Lightweight Optimized Detectioncited by 1
  4. 2025Designing Object Detection Models for TinyML: Foundations, Comparative Analysis, Challenges, and Emerging Solutionscited by 1
  5. 2024Fused attention mechanism-based ore sorting networkcited by 0
  6. 2025Improved YOLOv7x-Based Defect Detection Algorithm for Power Equipmentcited by 0
  7. 2025YOLO-FireAD: Efficient Fire Detection via Attention-Guided Inverted Residual Learning and Dual-Pooling Feature Preservationcited by 0
  8. 2026LAF-YOLOv10 with Partial Convolution Backbone, Attention-Guided Feature Pyramid, Auxiliary P2 Head, and Wise-IoU Loss for Small Object Detection in Drone Aerial Imagerycited by 0
  9. 2026Contour-Guided Spectral Routing for Robust Real-Time Pedestrian Detectioncited by 0
  10. 2026Small Object Detection in Drone Aerial Imagery with LAF-YOLOv10cited by 0