Tucker Bilinear Attention Network for Multi-scale Remote Sensing Object Detection

• URL: http://arxiv.org/abs/2303.05329v2
• Date: Sun, 28 May 2023 06:39:19 GMT
• Title: Tucker Bilinear Attention Network for Multi-scale Remote Sensing Object Detection
• Authors: Tao Chen, Ruirui Li, Jiafeng Fu, and Daguang Jiang
• Abstract summary: Large-scale variation of remote-sensing targets is one of main challenges in VHR remote-sensing object detection. This paper proposes two novel modules: Guided Attention and Tucker Bilinear Attention. Based on two modules, we build a new multi-scale remote sensing object detection framework.
• Score: 10.060030309684953
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Object detection on VHR remote sensing images plays a vital role in applications such as urban planning, land resource management, and rescue missions. The large-scale variation of the remote-sensing targets is one of the main challenges in VHR remote-sensing object detection. Existing methods improve the detection accuracy of high-resolution remote sensing objects by improving the structure of feature pyramids and adopting different attention modules. However, for small targets, there still be seriously missed detections due to the loss of key detail features. There is still room for improvement in the way of multiscale feature fusion and balance. To address this issue, this paper proposes two novel modules: Guided Attention and Tucker Bilinear Attention, which are applied to the stages of early fusion and late fusion respectively. The former can effectively retain clean key detail features, and the latter can better balance features through semantic-level correlation mining. Based on two modules, we build a new multi-scale remote sensing object detection framework. No bells and whistles. The proposed method largely improves the average precisions of small objects and achieves the highest mean average precisions compared with 9 state-of-the-art methods on DOTA, DIOR, and NWPU VHR-10.Code and models are available at https://github.com/Shinichict/GTNet.

Related papers

• Single-Point Supervised High-Resolution Dynamic Network for Infrared Small Target Detection [7.0456782736205685]
  We propose a single-point supervised high-resolution dynamic network (SSHD-Net) It achieves state-of-the-art (SOTA) detection performance using only single-point supervision. Experiments on the publicly available datasets NUDT-SIRST and IRSTD-1k demonstrate the effectiveness of our method.
  arXiv  Detail & Related papers  (2024-08-04T09:44:47Z)
• Visible and Clear: Finding Tiny Objects in Difference Map [50.54061010335082]
  We introduce a self-reconstruction mechanism in the detection model, and discover the strong correlation between it and the tiny objects. Specifically, we impose a reconstruction head in-between the neck of a detector, constructing a difference map of the reconstructed image and the input, which shows high sensitivity to tiny objects. We further develop a Difference Map Guided Feature Enhancement (DGFE) module to make the tiny feature representation more clear.
  arXiv  Detail & Related papers  (2024-05-18T12:22:26Z)
• Better Sampling, towards Better End-to-end Small Object Detection [7.7473020808686694]
  Small object detection remains unsatisfactory due to limited characteristics and high density and mutual overlap. We propose methods enhancing sampling within an end-to-end framework. Our model demonstrates a significant enhancement, achieving a 2.9% increase in average precision (AP) over the state-of-the-art (SOTA) on the VisDrone dataset.
  arXiv  Detail & Related papers  (2024-05-17T04:37:44Z)
• Small Object Detection by DETR via Information Augmentation and Adaptive Feature Fusion [4.9860018132769985]
  The RT-DETR model performs well in real-time object detection, but performs poorly in small object detection accuracy. We propose an adaptive feature fusion algorithm that assigns learnable parameters to each feature map from different levels. This enhances the model's ability to capture object features at different scales, thereby improving the accuracy of detecting small objects.
  arXiv  Detail & Related papers  (2024-01-16T00:01:23Z)
• Innovative Horizons in Aerial Imagery: LSKNet Meets DiffusionDet for Advanced Object Detection [55.2480439325792]
  We present an in-depth evaluation of an object detection model that integrates the LSKNet backbone with the DiffusionDet head. The proposed model achieves a mean average precision (MAP) of approximately 45.7%, which is a significant improvement. This advancement underscores the effectiveness of the proposed modifications and sets a new benchmark in aerial image analysis.
  arXiv  Detail & Related papers  (2023-11-21T19:49:13Z)
• Infrared Small-Dim Target Detection with Transformer under Complex Backgrounds [155.388487263872]
  We propose a new infrared small-dim target detection method with the transformer. We adopt the self-attention mechanism of the transformer to learn the interaction information of image features in a larger range. We also design a feature enhancement module to learn more features of small-dim targets.
  arXiv  Detail & Related papers  (2021-09-29T12:23:41Z)
• Multi-patch Feature Pyramid Network for Weakly Supervised Object Detection in Optical Remote Sensing Images [39.25541709228373]
  We propose a new architecture for object detection with a multiple patch feature pyramid network (MPFP-Net) MPFP-Net is different from the current models that during training only pursue the most discriminative patches. We introduce an effective method to regularize the residual values and make the fusion transition layers strictly norm-preserving.
  arXiv  Detail & Related papers  (2021-08-18T09:25:39Z)
• CFC-Net: A Critical Feature Capturing Network for Arbitrary-Oriented Object Detection in Remote Sensing Images [0.9462808515258465]
  In this paper, we discuss the role of discriminative features in object detection. We then propose a Critical Feature Capturing Network (CFC-Net) to improve detection accuracy. We show that our method achieves superior detection performance compared with many state-of-the-art approaches.
  arXiv  Detail & Related papers  (2021-01-18T02:31:09Z)
• MRDet: A Multi-Head Network for Accurate Oriented Object Detection in Aerial Images [51.227489316673484]
  We propose an arbitrary-oriented region proposal network (AO-RPN) to generate oriented proposals transformed from horizontal anchors. To obtain accurate bounding boxes, we decouple the detection task into multiple subtasks and propose a multi-head network. Each head is specially designed to learn the features optimal for the corresponding task, which allows our network to detect objects accurately.
  arXiv  Detail & Related papers  (2020-12-24T06:36:48Z)
• Anchor-free Small-scale Multispectral Pedestrian Detection [88.7497134369344]
  We propose a method for effective and efficient multispectral fusion of the two modalities in an adapted single-stage anchor-free base architecture. We aim at learning pedestrian representations based on object center and scale rather than direct bounding box predictions. Results show our method's effectiveness in detecting small-scaled pedestrians.
  arXiv  Detail & Related papers  (2020-08-19T13:13:01Z)
• NETNet: Neighbor Erasing and Transferring Network for Better Single Shot Object Detection [170.30694322460045]
  We propose a new Neighbor Erasing and Transferring (NET) mechanism to reconfigure the pyramid features and explore scale-aware features. A single-shot network called NETNet is constructed for scale-aware object detection.
  arXiv  Detail & Related papers  (2020-01-18T15:21:29Z)

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.