A Light-Weight Object Detection Framework with FPA Module for Optical Remote Sensing Imagery

• URL: http://arxiv.org/abs/2009.03063v1
• Date: Mon, 7 Sep 2020 12:41:17 GMT
• Title: A Light-Weight Object Detection Framework with FPA Module for Optical Remote Sensing Imagery
• Authors: Xi Gu, Lingbin Kong, Zhicheng Wang, Jie Li, Zhaohui Yu, Gang Wei
• Abstract summary: We propose an efficient anchor free object detector, CenterFPANet. To pursue speed, we use a lightweight backbone and introduce the asymmetric revolution block. This strategy can improve the accuracy of remote sensing image object detection without reducing the detection speed.
• Score: 12.762588615997624
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: With the development of remote sensing technology, the acquisition of remote sensing images is easier and easier, which provides sufficient data resources for the task of detecting remote sensing objects. However, how to detect objects quickly and accurately from many complex optical remote sensing images is a challenging hot issue. In this paper, we propose an efficient anchor free object detector, CenterFPANet. To pursue speed, we use a lightweight backbone and introduce the asymmetric revolution block. To improve the accuracy, we designed the FPA module, which links the feature maps of different levels, and introduces the attention mechanism to dynamically adjust the weights of each level of feature maps, which solves the problem of detection difficulty caused by large size range of remote sensing objects. This strategy can improve the accuracy of remote sensing image object detection without reducing the detection speed. On the DOTA dataset, CenterFPANet mAP is 64.00%, and FPS is 22.2, which is close to the accuracy of the anchor-based methods currently used and much faster than them. Compared with Faster RCNN, mAP is 6.76% lower but 60.87% faster. All in all, CenterFPANet achieves a balance between speed and accuracy in large-scale optical remote sensing object detection.

Related papers

• RemoteDet-Mamba: A Hybrid Mamba-CNN Network for Multi-modal Object Detection in Remote Sensing Images [13.98477009749389]
  We propose a multimodal remote sensing network that employs a quad-directional selective scanning fusion strategy called RemoteDet-Mamba. RemoteDet-Mamba simultaneously facilitates the learning of single-modal local features and the integration of patch-level global features. Experimental results on the DroneVehicle dataset demonstrate the effectiveness of RemoteDet-Mamba.
  arXiv  Detail & Related papers  (2024-10-17T13:20:20Z)
• Multi-Modal Neural Radiance Field for Monocular Dense SLAM with a Light-Weight ToF Sensor [58.305341034419136]
  We present the first dense SLAM system with a monocular camera and a light-weight ToF sensor. We propose a multi-modal implicit scene representation that supports rendering both the signals from the RGB camera and light-weight ToF sensor. Experiments demonstrate that our system well exploits the signals of light-weight ToF sensors and achieves competitive results.
  arXiv  Detail & Related papers  (2023-08-28T07:56:13Z)
• 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)
• Fewer is More: Efficient Object Detection in Large Aerial Images [59.683235514193505]
  This paper presents an Objectness Activation Network (OAN) to help detectors focus on fewer patches but achieve more efficient inference and more accurate results. Using OAN, all five detectors acquire more than 30.0% speed-up on three large-scale aerial image datasets. We extend our OAN to driving-scene object detection and 4K video object detection, boosting the detection speed by 112.1% and 75.0%, respectively.
  arXiv  Detail & Related papers  (2022-12-26T12:49:47Z)
• Fast Fourier Convolution Based Remote Sensor Image Object Detection for Earth Observation [0.0]
  We propose a Frequency-aware Feature Pyramid Framework (FFPF) for remote sensing object detection. F-ResNet is proposed to perceive the spectral context information by plugging the frequency domain convolution into each stage of the backbone. The BSFPN is designed to use a bilateral sampling strategy and skipping connection to better model the association of object features at different scales.
  arXiv  Detail & Related papers  (2022-09-01T15:50:58Z)
• Motion Robust High-Speed Light-Weighted Object Detection With Event Camera [24.192961837270172]
  We propose a motion robust and high-speed detection pipeline which better leverages the event data. Experiments on two typical real-scene event camera object detection datasets show that our method is competitive in terms of accuracy, efficiency, and the number of parameters.
  arXiv  Detail & Related papers  (2022-08-24T15:15:24Z)
• RRNet: Relational Reasoning Network with Parallel Multi-scale Attention for Salient Object Detection in Optical Remote Sensing Images [82.1679766706423]
  Salient object detection (SOD) for optical remote sensing images (RSIs) aims at locating and extracting visually distinctive objects/regions from the optical RSIs. We propose a relational reasoning network with parallel multi-scale attention for SOD in optical RSIs. Our proposed RRNet outperforms the existing state-of-the-art SOD competitors both qualitatively and quantitatively.
  arXiv  Detail & Related papers  (2021-10-27T07:18:32Z)
• 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)
• 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)
• PolarDet: A Fast, More Precise Detector for Rotated Target in Aerial Images [27.91544183861098]
  PolarDet is a fast and accurate one-stage object detector based on polar coordinate representation. Our detector introduces a sub-pixel center semantic structure to further improve classifying veracity. Our approach obtains the SOTA results on DOTA, UCAS-AOD, HRSC with 76.64% mAP, 97.01% mAP, and 90.46% mAP respectively.
  arXiv  Detail & Related papers  (2020-10-17T05:16:46Z)

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.