HCF-Net: Hierarchical Context Fusion Network for Infrared Small Object Detection

• URL: http://arxiv.org/abs/2403.10778v1
• Date: Sat, 16 Mar 2024 02:45:42 GMT
• Title: HCF-Net: Hierarchical Context Fusion Network for Infrared Small Object Detection
• Authors: Shibiao Xu, ShuChen Zheng, Wenhao Xu, Rongtao Xu, Changwei Wang, Jiguang Zhang, Xiaoqiang Teng, Ao Li, Li Guo,
• Abstract summary: We propose a deep learning method to improve infrared small object detection performance. The method includes the parallelized patch-aware attention (PPA) module, dimension-aware selective integration (DASI) module, and multi-dilated channel refiner (MDCR) module.
• Score: 16.92362922379821
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Infrared small object detection is an important computer vision task involving the recognition and localization of tiny objects in infrared images, which usually contain only a few pixels. However, it encounters difficulties due to the diminutive size of the objects and the generally complex backgrounds in infrared images. In this paper, we propose a deep learning method, HCF-Net, that significantly improves infrared small object detection performance through multiple practical modules. Specifically, it includes the parallelized patch-aware attention (PPA) module, dimension-aware selective integration (DASI) module, and multi-dilated channel refiner (MDCR) module. The PPA module uses a multi-branch feature extraction strategy to capture feature information at different scales and levels. The DASI module enables adaptive channel selection and fusion. The MDCR module captures spatial features of different receptive field ranges through multiple depth-separable convolutional layers. Extensive experimental results on the SIRST infrared single-frame image dataset show that the proposed HCF-Net performs well, surpassing other traditional and deep learning models. Code is available at https://github.com/zhengshuchen/HCFNet.

Related papers

• IRSAM: Advancing Segment Anything Model for Infrared Small Target Detection [55.554484379021524]
  Infrared Small Target Detection (IRSTD) task falls short in achieving satisfying performance due to a notable domain gap between natural and infrared images. We propose the IRSAM model for IRSTD, which improves SAM's encoder-decoder architecture to learn better feature representation of infrared small objects.
  arXiv  Detail & Related papers  (2024-07-10T10:17:57Z)
• Multi-Scale Direction-Aware Network for Infrared Small Target Detection [2.661766509317245]
  Infrared small target detection faces the problem that it is difficult to effectively separate the background and the target. We propose a multi-scale direction-aware network (MSDA-Net) to integrate the high-frequency directional features of infrared small targets. MSDA-Net achieves state-of-the-art (SOTA) results on the public NUDT-SIRST, SIRST and IRSTD-1k datasets.
  arXiv  Detail & Related papers  (2024-06-04T07:23:09Z)
• Hi-ResNet: Edge Detail Enhancement for High-Resolution Remote Sensing Segmentation [10.919956120261539]
  High-resolution remote sensing (HRS) semantic segmentation extracts key objects from high-resolution coverage areas. objects of the same category within HRS images show significant differences in scale and shape across diverse geographical environments. We propose a High-resolution remote sensing network (Hi-ResNet) with efficient network structure designs.
  arXiv  Detail & Related papers  (2023-05-22T03:58:25Z)
• 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)
• UIU-Net: U-Net in U-Net for Infrared Small Object Detection [36.72184013409837]
  We propose a simple and effective U-Net in U-Net'' framework, UIU-Net for short, and detect small objects in infrared images. As the name suggests, UIU-Net embeds a tiny U-Net into a larger U-Net backbone, enabling the multi-level and multi-scale representation learning of objects. The proposed UIU-Net also produces powerful generalization performance for video sequence infrared small object datasets.
  arXiv  Detail & Related papers  (2022-12-02T04:52:26Z)
• A lightweight multi-scale context network for salient object detection in optical remote sensing images [16.933770557853077]
  We propose a multi-scale context network, namely MSCNet, for salient object detection in optical RSIs. Specifically, a multi-scale context extraction module is adopted to address the scale variation of salient objects. In order to accurately detect complete salient objects in complex backgrounds, we design an attention-based pyramid feature aggregation mechanism.
  arXiv  Detail & Related papers  (2022-05-18T14:32:47Z)
• Multi-Content Complementation Network for Salient Object Detection in Optical Remote Sensing Images [108.79667788962425]
  salient object detection in optical remote sensing images (RSI-SOD) remains to be a challenging emerging topic. We propose a novel Multi-Content Complementation Network (MCCNet) to explore the complementarity of multiple content for RSI-SOD. In MCCM, we consider multiple types of features that are critical to RSI-SOD, including foreground features, edge features, background features, and global image-level features.
  arXiv  Detail & Related papers  (2021-12-02T04:46:40Z)
• 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)
• Siamese Network for RGB-D Salient Object Detection and Beyond [113.30063105890041]
  A novel framework is proposed to learn from both RGB and depth inputs through a shared network backbone. Comprehensive experiments using five popular metrics show that the designed framework yields a robust RGB-D saliency detector. We also link JL-DCF to the RGB-D semantic segmentation field, showing its capability of outperforming several semantic segmentation models.
  arXiv  Detail & Related papers  (2020-08-26T06:01:05Z)
• A Single Stream Network for Robust and Real-time RGB-D Salient Object Detection [89.88222217065858]
  We design a single stream network to use the depth map to guide early fusion and middle fusion between RGB and depth. This model is 55.5% lighter than the current lightest model and runs at a real-time speed of 32 FPS when processing a $384 times 384$ image.
  arXiv  Detail & Related papers  (2020-07-14T04:40:14Z)

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.