FPDIoU Loss: A Loss Function for Efficient Bounding Box Regression of Rotated Object Detection

• URL: http://arxiv.org/abs/2405.09942v2
• Date: Sun, 19 May 2024 04:32:53 GMT
• Title: FPDIoU Loss: A Loss Function for Efficient Bounding Box Regression of Rotated Object Detection
• Authors: Siliang Ma, Yong Xu,
• Abstract summary: We propose a novel metric for arbitrary shapes comparison based on minimum points distance. $FPDIoU$ loss has been applied to state-of-the-art rotated object detection.
• Score: 10.655167287088368
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Bounding box regression is one of the important steps of object detection. However, rotation detectors often involve a more complicated loss based on SkewIoU which is unfriendly to gradient-based training. Most of the existing loss functions for rotated object detection calculate the difference between two bounding boxes only focus on the deviation of area or each points distance (e.g., $\mathcal{L}_{Smooth-\ell 1}$, $\mathcal{L}_{RotatedIoU}$ and $\mathcal{L}_{PIoU}$). The calculation process of some loss functions is extremely complex (e.g. $\mathcal{L}_{KFIoU}$). In order to improve the efficiency and accuracy of bounding box regression for rotated object detection, we proposed a novel metric for arbitrary shapes comparison based on minimum points distance, which takes most of the factors from existing loss functions for rotated object detection into account, i.e., the overlap or nonoverlapping area, the central points distance and the rotation angle. We also proposed a loss function called $\mathcal{L}_{FPDIoU}$ based on four points distance for accurate bounding box regression focusing on faster and high quality anchor boxes. In the experiments, $FPDIoU$ loss has been applied to state-of-the-art rotated object detection (e.g., RTMDET, H2RBox) models training with three popular benchmarks of rotated object detection including DOTA, DIOR, HRSC2016 and two benchmarks of arbitrary orientation scene text detection including ICDAR 2017 RRC-MLT and ICDAR 2019 RRC-MLT, which achieves better performance than existing loss functions.

Related papers

• MPDIoU: A Loss for Efficient and Accurate Bounding Box Regression [0.0]
  We propose a novel bounding box similarity comparison metric MPDIoU. 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.
  arXiv  Detail & Related papers  (2023-07-14T23:54:49Z)
• Detecting Rotated Objects as Gaussian Distributions and Its 3-D Generalization [81.29406957201458]
  Existing detection methods commonly use a parameterized bounding box (BBox) to model and detect (horizontal) objects. We argue that such a mechanism has fundamental limitations in building an effective regression loss for rotation detection. We propose to model the rotated objects as Gaussian distributions. We extend our approach from 2-D to 3-D with a tailored algorithm design to handle the heading estimation.
  arXiv  Detail & Related papers  (2022-09-22T07:50:48Z)
• The KFIoU Loss for Rotated Object Detection [115.334070064346]
  In this paper, we argue that one effective alternative is to devise an approximate loss who can achieve trend-level alignment with SkewIoU loss. Specifically, we model the objects as Gaussian distribution and adopt Kalman filter to inherently mimic the mechanism of SkewIoU. The resulting new loss called KFIoU is easier to implement and works better compared with exact SkewIoU.
  arXiv  Detail & Related papers  (2022-01-29T10:54:57Z)
• RSDet++: Point-based Modulated Loss for More Accurate Rotated Object Detection [53.57176614020894]
  We classify the discontinuity of loss in both five-param and eight-param rotated object detection methods as rotation sensitivity error (RSE) We introduce a novel modulated rotation loss to alleviate the problem and propose a rotation sensitivity detection network (RSDet) To further improve the accuracy of our method on objects smaller than 10 pixels, we introduce a novel RSDet++.
  arXiv  Detail & Related papers  (2021-09-24T11:57:53Z)
• Learning High-Precision Bounding Box for Rotated Object Detection via Kullback-Leibler Divergence [100.6913091147422]
  Existing rotated object detectors are mostly inherited from the horizontal detection paradigm. In this paper, we are motivated to change the design of rotation regression loss from induction paradigm to deduction methodology.
  arXiv  Detail & Related papers  (2021-06-03T14:29:19Z)
• Optimization for Oriented Object Detection via Representation Invariance Loss [2.501282372971187]
  mainstream rotation detectors use oriented bounding boxes (OBB) or quadrilateral bounding boxes (QBB) to represent the rotating objects. We propose a Representation Invariance Loss (RIL) to optimize the bounding box regression for the rotating objects. Our method achieves consistent and substantial improvement in experiments on remote sensing datasets and scene text datasets.
  arXiv  Detail & Related papers  (2021-03-22T07:55:33Z)
• Rethinking Rotated Object Detection with Gaussian Wasserstein Distance Loss [111.8807588392563]
  Boundary discontinuity and its inconsistency to the final detection metric have been the bottleneck for rotating detection regression loss design. We propose a novel regression loss based on Gaussian Wasserstein distance as a fundamental approach to solve the problem.
  arXiv  Detail & Related papers  (2021-01-28T12:04:35Z)
• Dense Label Encoding for Boundary Discontinuity Free Rotation Detection [69.75559390700887]
  This paper explores a relatively less-studied methodology based on classification. We propose new techniques to push its frontier in two aspects. Experiments and visual analysis on large-scale public datasets for aerial images show the effectiveness of our approach.
  arXiv  Detail & Related papers  (2020-11-19T05:42:02Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.