Dense Multiscale Feature Fusion Pyramid Networks for Object Detection in UAV-Captured Images

• URL: http://arxiv.org/abs/2012.10643v1
• Date: Sat, 19 Dec 2020 10:05:31 GMT
• Title: Dense Multiscale Feature Fusion Pyramid Networks for Object Detection in UAV-Captured Images
• Authors: Yingjie Liu
• Abstract summary: We propose a novel method called Dense Multiscale Feature Fusion Pyramid Networks(DMFFPN), which is aimed at obtaining rich features as much as possible. Specifically, the dense connection is designed to fully utilize the representation from the different convolutional layers. Experiments on the drone-based datasets named VisDrone-DET suggest a competitive performance of our method.
• Score: 0.09065034043031667
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Although much significant progress has been made in the research field of object detection with deep learning, there still exists a challenging task for the objects with small size, which is notably pronounced in UAV-captured images. Addressing these issues, it is a critical need to explore the feature extraction methods that can extract more sufficient feature information of small objects. In this paper, we propose a novel method called Dense Multiscale Feature Fusion Pyramid Networks(DMFFPN), which is aimed at obtaining rich features as much as possible, improving the information propagation and reuse. Specifically, the dense connection is designed to fully utilize the representation from the different convolutional layers. Furthermore, cascade architecture is applied in the second stage to enhance the localization capability. Experiments on the drone-based datasets named VisDrone-DET suggest a competitive performance of our method.

Related papers

• PVAFN: Point-Voxel Attention Fusion Network with Multi-Pooling Enhancing for 3D Object Detection [59.355022416218624]
  integration of point and voxel representations is becoming more common in LiDAR-based 3D object detection. We propose a novel two-stage 3D object detector, called Point-Voxel Attention Fusion Network (PVAFN) PVAFN uses a multi-pooling strategy to integrate both multi-scale and region-specific information effectively.
  arXiv  Detail & Related papers  (2024-08-26T19:43:01Z)
• PGNeXt: High-Resolution Salient Object Detection via Pyramid Grafting Network [24.54269823691119]
  We present an advanced study on more challenging high-resolution salient object detection (HRSOD) from both dataset and network framework perspectives. To compensate for the lack of HRSOD dataset, we thoughtfully collect a large-scale high resolution salient object detection dataset, called UHRSD. All the images are finely annotated in pixel-level, far exceeding previous low-resolution SOD datasets.
  arXiv  Detail & Related papers  (2024-08-02T09:31:21Z)
• AMANet: Advancing SAR Ship Detection with Adaptive Multi-Hierarchical Attention Network [0.5437298646956507]
  A novel adaptive multi-hierarchical attention module (AMAM) is proposed to learn multi-scale features and adaptively aggregate salient features from various feature layers. We first fuse information from adjacent feature layers to enhance the detection of smaller targets, thereby achieving multi-scale feature enhancement. Thirdly, we present a novel adaptive multi-hierarchical attention network (AMANet) by embedding the AMAM between the backbone network and the feature pyramid network.
  arXiv  Detail & Related papers  (2024-01-24T03:56:33Z)
• Enhanced Single-shot Detector for Small Object Detection in Remote Sensing Images [33.84369068593722]
  We propose image pyramid single-shot detector (IPSSD) for small-scale object detection. In IPSSD, single-shot detector is adopted combined with an image pyramid network to extract semantically strong features for generating candidate regions. The proposed network can enhance the small-scale features from a feature pyramid network.
  arXiv  Detail & Related papers  (2022-05-12T07:35:07Z)
• Joint Learning of Salient Object Detection, Depth Estimation and Contour Extraction [91.43066633305662]
  We propose a novel multi-task and multi-modal filtered transformer (MMFT) network for RGB-D salient object detection (SOD) Specifically, we unify three complementary tasks: depth estimation, salient object detection and contour estimation. The multi-task mechanism promotes the model to learn the task-aware features from the auxiliary tasks. Experiments show that it not only significantly surpasses the depth-based RGB-D SOD methods on multiple datasets, but also precisely predicts a high-quality depth map and salient contour at the same time.
  arXiv  Detail & Related papers  (2022-03-09T17:20:18Z)
• 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)
• AdaZoom: Adaptive Zoom Network for Multi-Scale Object Detection in Large Scenes [57.969186815591186]
  Detection in large-scale scenes is a challenging problem due to small objects and extreme scale variation. We propose a novel Adaptive Zoom (AdaZoom) network as a selective magnifier with flexible shape and focal length to adaptively zoom the focus regions for object detection.
  arXiv  Detail & Related papers  (2021-06-19T03:30:22Z)
• EDN: Salient Object Detection via Extremely-Downsampled Network [66.38046176176017]
  We introduce an Extremely-Downsampled Network (EDN), which employs an extreme downsampling technique to effectively learn a global view of the whole image. Experiments demonstrate that EDN achieves sArt performance with real-time speed.
  arXiv  Detail & Related papers  (2020-12-24T04:23:48Z)
• Underwater object detection using Invert Multi-Class Adaboost with deep learning [37.14538666012363]
  We propose a novel neural network architecture, namely Sample-WeIghted hyPEr Network (SWIPENet), for small object detection. We show that the proposed SWIPENet+IMA framework achieves better performance in detection accuracy against several state-of-the-art object detection approaches.
  arXiv  Detail & Related papers  (2020-05-23T15:30:38Z)
• Cross-layer Feature Pyramid Network for Salient Object Detection [102.20031050972429]
  We propose a novel Cross-layer Feature Pyramid Network to improve the progressive fusion in salient object detection. The distributed features per layer own both semantics and salient details from all other layers simultaneously, and suffer reduced loss of important information.
  arXiv  Detail & Related papers  (2020-02-25T14:06:27Z)

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.