RotaTR: Detection Transformer for Dense and Rotated Object

• URL: http://arxiv.org/abs/2312.02821v1
• Date: Tue, 5 Dec 2023 15:06:04 GMT
• Title: RotaTR: Detection Transformer for Dense and Rotated Object
• Authors: Zhu Yuke, Ruan Yumeng, Yang Lei, Guo Sheng
• Abstract summary: We propose Rotated object detection TRansformer (RotaTR) as an extension of DETR to oriented detection. Specifically, we design Rotation Sensitive deformable (RSDeform) attention to enhance the DETR's ability to detect oriented targets. RotaTR shows a great advantage in detecting dense and oriented objects compared to the original DETR.
• Score: 0.49764328892172144
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Detecting the objects in dense and rotated scenes is a challenging task. Recent works on this topic are mostly based on Faster RCNN or Retinanet. As they are highly dependent on the pre-set dense anchors and the NMS operation, the approach is indirect and suboptimal.The end-to-end DETR-based detectors have achieved great success in horizontal object detection and many other areas like segmentation, tracking, action recognition and etc.However, the DETR-based detectors perform poorly on dense rotated target tasks and perform worse than most modern CNN-based detectors. In this paper, we find the most significant reason for the poor performance is that the original attention can not accurately focus on the oriented targets. Accordingly, we propose Rotated object detection TRansformer (RotaTR) as an extension of DETR to oriented detection. Specifically, we design Rotation Sensitive deformable (RSDeform) attention to enhance the DETR's ability to detect oriented targets. It is used to build the feature alignment module and rotation-sensitive decoder for our model. We test RotaTR on four challenging-oriented benchmarks. It shows a great advantage in detecting dense and oriented objects compared to the original DETR. It also achieves competitive results when compared to the state-of-the-art.

Related papers

• Investigating the Robustness and Properties of Detection Transformers (DETR) Toward Difficult Images [1.5727605363545245]
  Transformer-based object detectors (DETR) have shown significant performance across machine vision tasks. The critical issue to be addressed is how this model architecture can handle different image nuisances. We studied this issue by measuring the performance of DETR with different experiments and benchmarking the network.
  arXiv  Detail & Related papers  (2023-10-12T23:38:52Z)
• Object Detection with Transformers: A Review [11.255962936937744]
  This paper provides a comprehensive review of 21 recently proposed advancements in the original DETR model. We conduct a comparative analysis across various detection transformers, evaluating their performance and network architectures. We hope that this study will ignite further interest among researchers in addressing the existing challenges and exploring the application of transformers in the object detection domain.
  arXiv  Detail & Related papers  (2023-06-07T16:13:38Z)
• Adaptive Rotated Convolution for Rotated Object Detection [96.94590550217718]
  We present Adaptive Rotated Convolution (ARC) module to handle rotated object detection problem. In our ARC module, the convolution kernels rotate adaptively to extract object features with varying orientations in different images. The proposed approach achieves state-of-the-art performance on the DOTA dataset with 81.77% mAP.
  arXiv  Detail & Related papers  (2023-03-14T11:53:12Z)
• Point RCNN: An Angle-Free Framework for Rotated Object Detection [13.209895262511015]
  Rotated object detection in aerial images is still challenging due to arbitrary orientations, large scale and aspect ratio variations, and extreme density of objects. We propose a purely angle-free framework for rotated object detection, called Point RCNN, which mainly consists of PointRPN and PointReg. Experiments demonstrate that our Point RCNN achieves the new state-of-the-art detection performance on commonly used aerial datasets.
  arXiv  Detail & Related papers  (2022-05-28T04:07:37Z)
• Recurrent Glimpse-based Decoder for Detection with Transformer [85.64521612986456]
  We introduce a novel REcurrent Glimpse-based decOder (REGO) in this paper. In particular, the REGO employs a multi-stage recurrent processing structure to help the attention of DETR gradually focus on foreground objects. REGO consistently boosts the performance of different DETR detectors by up to 7% relative gain at the same setting of 50 training epochs.
  arXiv  Detail & Related papers  (2021-12-09T00:29:19Z)
• Anchor-free Oriented Proposal Generator for Object Detection [59.54125119453818]
  Oriented object detection is a practical and challenging task in remote sensing image interpretation. Nowadays, oriented detectors mostly use horizontal boxes as intermedium to derive oriented boxes from them. We propose a novel Anchor-free Oriented Proposal Generator (AOPG) that abandons the horizontal boxes-related operations from the network architecture.
  arXiv  Detail & Related papers  (2021-10-05T10:45:51Z)
• 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)
• Oriented Object Detection with Transformer [51.634913687632604]
  We implement Oriented Object DEtection with TRansformer ($bf O2DETR$) based on an end-to-end network. We design a simple but highly efficient encoder for Transformer by replacing the attention mechanism with depthwise separable convolution. Our $rm O2DETR$ can be another new benchmark in the field of oriented object detection, which achieves up to 3.85 mAP improvement over Faster R-CNN and RetinaNet.
  arXiv  Detail & Related papers  (2021-06-06T14:57:17Z)
• OSKDet: Towards Orientation-sensitive Keypoint Localization for Rotated Object Detection [0.0]
  We propose an orientation-sensitive keypoint based rotated detector OSKDet. We adopt a set of keypoints to characterize the target and predict the keypoint heatmap on ROI to form a rotated target. We achieve an AP of 77.81% on DOTA, 89.91% on HRSC2016, and 97.18% on UCAS-AOD, respectively.
  arXiv  Detail & Related papers  (2021-04-18T03:40:52Z)
• End-to-End Object Detection with Transformers [88.06357745922716]
  We present a new method that views object detection as a direct set prediction problem. Our approach streamlines the detection pipeline, effectively removing the need for many hand-designed components. The main ingredients of the new framework, called DEtection TRansformer or DETR, are a set-based global loss.
  arXiv  Detail & Related papers  (2020-05-26T17:06:38Z)

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.