Unbiased IoU for Spherical Image Object Detection

• URL: http://arxiv.org/abs/2108.08029v1
• Date: Wed, 18 Aug 2021 08:18:37 GMT
• Title: Unbiased IoU for Spherical Image Object Detection
• Authors: Qiang Zhao, Bin Chen, Hang Xu, Yike Ma, Xiaodong Li, Bailan Feng, Chenggang Yan, Feng Dai
• Abstract summary: We first identify that spherical rectangles are unbiased bounding boxes for objects in spherical images, and then propose an analytical method for IoU calculation without any approximations. Based on the unbiased representation and calculation, we also present an anchor free object detection algorithm for spherical images.
• Score: 45.17996641893818
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: As one of the most fundamental and challenging problems in computer vision, object detection tries to locate object instances and find their categories in natural images. The most important step in the evaluation of object detection algorithm is calculating the intersection-over-union (IoU) between the predicted bounding box and the ground truth one. Although this procedure is well-defined and solved for planar images, it is not easy for spherical image object detection. Existing methods either compute the IoUs based on biased bounding box representations or make excessive approximations, thus would give incorrect results. In this paper, we first identify that spherical rectangles are unbiased bounding boxes for objects in spherical images, and then propose an analytical method for IoU calculation without any approximations. Based on the unbiased representation and calculation, we also present an anchor free object detection algorithm for spherical images. The experiments on two spherical object detection datasets show that the proposed method can achieve better performance than existing methods.

Related papers

• Weighted Circle Fusion: Ensembling Circle Representation from Different Object Detection Results [9.540862304334969]
  We propose Weighted Circle Fusion (WCF), a simple approach for merging predictions from various circle detection models. Our method undergoes thorough evaluation on a proprietary dataset for glomerular detection in object detection within whole slide imaging (WSI) The findings reveal a performance gain of 5 %, respectively, compared to existing ensemble methods.
  arXiv  Detail & Related papers  (2024-06-27T21:34:51Z)
• YOLC: You Only Look Clusters for Tiny Object Detection in Aerial Images [33.80392696735718]
  YOLC (You Only Look Clusters) is an efficient and effective framework that builds on an anchor-free object detector, CenterNet. To overcome the challenges posed by large-scale images and non-uniform object distribution, we introduce a Local Scale Module (LSM) that adaptively searches cluster regions for zooming in for accurate detection. We perform extensive experiments on two aerial image datasets, including Visdrone 2019 and UAVDT, to demonstrate the effectiveness and superiority of our proposed approach.
  arXiv  Detail & Related papers  (2024-04-09T10:03:44Z)
• SalienDet: A Saliency-based Feature Enhancement Algorithm for Object Detection for Autonomous Driving [160.57870373052577]
  We propose a saliency-based OD algorithm (SalienDet) to detect unknown objects. Our SalienDet utilizes a saliency-based algorithm to enhance image features for object proposal generation. We design a dataset relabeling approach to differentiate the unknown objects from all objects in training sample set to achieve Open-World Detection.
  arXiv  Detail & Related papers  (2023-05-11T16:19:44Z)
• Rigidity-Aware Detection for 6D Object Pose Estimation [60.88857851869196]
  Most recent 6D object pose estimation methods first use object detection to obtain 2D bounding boxes before actually regressing the pose. We propose a rigidity-aware detection method exploiting the fact that, in 6D pose estimation, the target objects are rigid. Key to the success of our approach is a visibility map, which we propose to build using a minimum barrier distance between every pixel in the bounding box and the box boundary.
  arXiv  Detail & Related papers  (2023-03-22T09:02:54Z)
• ARUBA: An Architecture-Agnostic Balanced Loss for Aerial Object Detection [24.085715205081385]
  We denote size of an object as the number of pixels it covers in an image and size imbalance as the over-representation of certain sizes of objects in a dataset. We propose a novel ARchitectUre-agnostic BAlanced Loss (ARUBA) that can be applied as a plugin on top of any object detection model.
  arXiv  Detail & Related papers  (2022-10-10T11:28:16Z)
• Object Detection in Aerial Images with Uncertainty-Aware Graph Network [61.02591506040606]
  We propose a novel uncertainty-aware object detection framework with a structured-graph, where nodes and edges are denoted by objects. We refer to our model as Uncertainty-Aware Graph network for object DETection (UAGDet)
  arXiv  Detail & Related papers  (2022-08-23T07:29:03Z)
• Scale Normalized Image Pyramids with AutoFocus for Object Detection [75.71320993452372]
  A scale normalized image pyramid (SNIP) is generated that, like human vision, only attends to objects within a fixed size range at different scales. We propose an efficient spatial sub-sampling scheme which only operates on fixed-size sub-regions likely to contain objects. The resulting algorithm is referred to as AutoFocus and results in a 2.5-5 times speed-up during inference when used with SNIP.
  arXiv  Detail & Related papers  (2021-02-10T18:57:53Z)
• Slender Object Detection: Diagnoses and Improvements [74.40792217534]
  In this paper, we are concerned with the detection of a particular type of objects with extreme aspect ratios, namely textbfslender objects. For a classical object detection method, a drastic drop of $18.9%$ mAP on COCO is observed, if solely evaluated on slender objects.
  arXiv  Detail & Related papers  (2020-11-17T09:39:42Z)
• Object condensation: one-stage grid-free multi-object reconstruction in physics detectors, graph and image data [0.0]
  A new object condensation method is proposed for detector signals. The method is based on non-image-like data structures, such as graphs and point clouds. It is applied to a simple object classification problem in images and used to reconstruct multiple particles from detector signals.
  arXiv  Detail & Related papers  (2020-02-10T09:04:02Z)

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.