Extended Feature Pyramid Network for Small Object Detection

• URL: http://arxiv.org/abs/2003.07021v2
• Date: Thu, 9 Apr 2020 12:49:50 GMT
• Title: Extended Feature Pyramid Network for Small Object Detection
• Authors: Chunfang Deng, Mengmeng Wang, Liang Liu, Yong Liu
• Abstract summary: We propose extended feature pyramid network (EFPN) with an extra high-resolution pyramid level specialized for small object detection. Specifically, we design a novel module, named feature texture transfer (FTT), which is used to super-resolve features and extract credible regional details simultaneously. In our experiments, the proposed EFPN is efficient on both computation and memory, and yields state-of-the-art results.
• Score: 20.029591259254847
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Small object detection remains an unsolved challenge because it is hard to extract information of small objects with only a few pixels. While scale-level corresponding detection in feature pyramid network alleviates this problem, we find feature coupling of various scales still impairs the performance of small objects. In this paper, we propose extended feature pyramid network (EFPN) with an extra high-resolution pyramid level specialized for small object detection. Specifically, we design a novel module, named feature texture transfer (FTT), which is used to super-resolve features and extract credible regional details simultaneously. Moreover, we design a foreground-background-balanced loss function to alleviate area imbalance of foreground and background. In our experiments, the proposed EFPN is efficient on both computation and memory, and yields state-of-the-art results on small traffic-sign dataset Tsinghua-Tencent 100K and small category of general object detection dataset MS COCO.

Related papers

• Renormalized Connection for Scale-preferred Object Detection in Satellite Imagery [51.83786195178233]
  We design a Knowledge Discovery Network (KDN) to implement the renormalization group theory in terms of efficient feature extraction. Renormalized connection (RC) on the KDN enables synergistic focusing'' of multi-scale features. RCs extend the multi-level feature's divide-and-conquer'' mechanism of the FPN-based detectors to a wide range of scale-preferred tasks.
  arXiv  Detail & Related papers  (2024-09-09T13:56:22Z)
• Small Object Tracking in LiDAR Point Cloud: Learning the Target-awareness Prototype and Fine-grained Search Region [4.515917777731159]
  We propose a Siamese network-based method for small object tracking in the LiDAR point cloud. Our method can effectively improve the tracking performance of small targets without affecting normal-sized objects.
  arXiv  Detail & Related papers  (2024-01-24T08:02:44Z)
• 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)
• Small Object Detection via Coarse-to-fine Proposal Generation and Imitation Learning [52.06176253457522]
  We propose a two-stage framework tailored for small object detection based on the Coarse-to-fine pipeline and Feature Imitation learning. CFINet achieves state-of-the-art performance on the large-scale small object detection benchmarks, SODA-D and SODA-A.
  arXiv  Detail & Related papers  (2023-08-18T13:13:09Z)
• 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)
• High-resolution Iterative Feedback Network for Camouflaged Object Detection [128.893782016078]
  Spotting camouflaged objects that are visually assimilated into the background is tricky for object detection algorithms. We aim to extract the high-resolution texture details to avoid the detail degradation that causes blurred vision in edges and boundaries. We introduce a novel HitNet to refine the low-resolution representations by high-resolution features in an iterative feedback manner.
  arXiv  Detail & Related papers  (2022-03-22T11:20:21Z)
• Context-Preserving Instance-Level Augmentation and Deformable Convolution Networks for SAR Ship Detection [50.53262868498824]
  Shape deformation of targets in SAR image due to random orientation and partial information loss is an essential challenge in SAR ship detection. We propose a data augmentation method to train a deep network that is robust to partial information loss within the targets.
  arXiv  Detail & Related papers  (2022-02-14T07:01:01Z)
• Dense Multiscale Feature Fusion Pyramid Networks for Object Detection in UAV-Captured Images [0.09065034043031667]
  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.
  arXiv  Detail & Related papers  (2020-12-19T10:05:31Z)
• Effective Fusion Factor in FPN for Tiny Object Detection [12.241778953479226]
  We argue that the top-down connections between adjacent layers in FPN bring two-side influences for tiny object detection. We propose a novel concept, fusion factor, to control information that deep layers deliver to shallow layers. We show that when configuring FPN with a proper fusion factor, the network is able to achieve significant performance gains.
  arXiv  Detail & Related papers  (2020-11-04T13:55:10Z)
• 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.