Info-FPN: An Informative Feature Pyramid Network for object detection in remote sensing images

Chen, Silin; Zhao, Jiaqi; Zhou, Yong; Wang, Hanzheng; Yao, Rui; Zhang, Lixu; Xue, Yong

doi:10.1016/j.eswa.2022.119132

Cited by 33 publications

(12 citation statements)

References 60 publications

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“…Problem solved Optimization Strategies CF2PN [84] Cross-level information exchange Cross-scale fusion module (CSFM) HawkNet [85] Cross-level information exchange Gated context-aware module (G-CAM) FPN-based [86] Cross-level information exchange Receiver field expansion block ABNet [87] Cross-level information exchange Adaptively combining multiscale features MSGN [88] Cross-level information exchange Backward semantic guided filtering (BSGF) MFEPN [89] Cross-level information exchange CAFUS+FEM SCANet [90] Inadequate expression RFEM+SCFM MVNet [91] Inadequate expression MRBs+MRFEM MSFC-Net [92] Inadequate expression Composite Semantic Feature Fusion (CSFF) SRAF-Net [93] Inadequate expression SE-FPN+SADH VSRNet [94] Inadequate expression VRG+SRG mSODANet [95] Inadequate expression Dilation Convolution for multiple scales SGFTHR [96] Channel information loss Structure Guided Feature Transformation (SGFT) MFAF [97] Channel information loss FI+SAW+CSP M2S [98] Channel information loss Improving feature extraction and feature refinement Info-FPN [99] Feature misalignment Feature alignment module (FAM) FDLR-Net [100] Feature misalignment Localization refinement module (LRM) SME-Net [101] Feature misalignment Feature splitting and merging module (FSM) GLNet [102] Feature misalignment MSP+SA HyNet [103] Feature misalignment Learning hyperscale feature representations ASSD [104] Feature misalignment Pseudo-Anchor Proposal Module (PAPM) VistrongerDet [105] Feature misalignment FPN-level, ROI-level, and head-level enhancements HRDNet [106] Feature misalignment MD-IPN+MS-FPN GLGCNet [107] Restricted local information Saliency enhancement modules DCL-Net [108] Restricted local information RFAM+PAM SMSR [109] Restricted local information Aggregating features learned at different depths GLSAN [24] Crowded targets GLDN+SARSA+LSRN GDF-Net [110] Crowded targets Global density model (GDM) GSNet [111] Crowded targets Feat...…”

Section: Methodsmentioning

confidence: 99%

A Survey of Small Object Detection Based on Deep Learning in Aerial Images

Wang

Chen

2023

Preprint

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Small object detection poses a formidable challenge in the field of computer vision, particularly when it comes to analyzing aerial remote sensing images. Despite the rapid development of deep learning and significant progress in detection techniques in natural scenes, the migration of these algorithms to aerial images has not met expectations. This is primarily due to limitations in imaging acquisition conditions, including small target size, viewpoint specificity, background complexity, as well as scale and orientation diversity. Although the increasing application of deep learning-based algorithms to overcome these problems, few studies have summarized the optimization of different deep learning strategies used for small target detection in aerial images. Therefore, this paper aims to explore the application of deep learning methods for small object detection in aerial images. The primary challenges in small object detection in aerial images will be summarized. Next, a meticulous analysis and categorization of the prevailing deep learning optimization strategies employed to surmount the challenges encountered in aerial image detection is undertaken. Following that, we provide a comprehensive presentation of the object detection datasets utilized in aerial remote sensing images, along with the evaluation metrics employed. Additionally, we furnish experimental data pertaining to the currently proposed detection algorithms. Finally, the advantages and disadvantages of various optimization strategies and potential development trends are discussed. Hopefully, it can provide a reference for researchers in this field.

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Section: Methodsmentioning

confidence: 99%

A Survey of Small Object Detection Based on Deep Learning in Aerial Images

Wang

Chen

2023

Preprint

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“…Gao et al [89] proposed a global-to-local (GL) network, which achieved accurate and robust remote sensing object detection via global and local feature fusion. Chen et al [90] proposed the Info-FPN network improved the remote sensing image object detection algorithm via an information transfer mechanism and an adaptive weighted loss function. Su et al [91] proposed the multi-scale context-aware RCNN method, which showed high accuracy and stability in detecting small sample objects in remote sensing images.…”

Section: Remote Sensing Image Object Detection Methods Based On Multi...mentioning

confidence: 99%

“…Remote sensing object detection and multi-scale object detection tasks. Chen et al [90] An innovative design with an information feature pyramid and feature selection based on information gain.…”

Section: Gao Et Al [89]mentioning

confidence: 99%

A Review: Remote Sensing Image Object Detection Algorithm Based on Deep Learning

Bai,

2023

Electronics

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Target detection in optical remote sensing images using deep-learning technologies has a wide range of applications in urban building detection, road extraction, crop monitoring, and forest fire monitoring, which provides strong support for environmental monitoring, urban planning, and agricultural management. This paper reviews the research progress of the YOLO series, SSD series, candidate region series, and Transformer algorithm. It summarizes the object detection algorithms based on standard improvement methods such as supervision, attention mechanism, and multi-scale. The performance of different algorithms is also compared and analyzed with the common remote sensing image data sets. Finally, future research challenges, improvement directions, and issues of concern are prospected, which provides valuable ideas for subsequent related research.

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“…EfficientDet [28] achieves multi-scale feature extraction through a bidirectional feature pyramid network (BiFPN), which produces better performance and efficiency. An informative feature pyramid network (Info-FPN) [6] was proposed to address channel information loss, feature misalignment, and aliasing effects.…”

Section: Extraction and Fusion Of Multi-scale Featuresmentioning

confidence: 99%

“…With the rapid development of remote sensing technology, object detection in remote sensing images has emerged as a burgeoning research area in computer vision. Various studies have focused on utilizing deep-learning-based object detection methods in the domain of remote sensing [1][2][3][4][5][6]. However, detecting targets in these images has shown itself to be challenging due to the objects' varying scales and resolutions.…”

Section: Introductionmentioning

confidence: 99%

Object Detection in Remote Sensing Images Based on Adaptive Multi-Scale Feature Fusion Method

Liu,

Zhang,

et al. 2024

Remote Sensing

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Multi-scale object detection is critical for analyzing remote sensing images. Traditional feature pyramid networks, which are aimed at accommodating objects of varying sizes through multi-level feature extraction, face significant challenges due to the diverse scale variations present in remote sensing images. This situation often forces single-level features to span a broad spectrum of object sizes, complicating accurate localization and classification. To tackle these challenges, this paper proposes an innovative algorithm that incorporates an adaptive multi-scale feature enhancement and fusion module (ASEM), which enhances remote sensing image object detection through sophisticated multi-scale feature fusion. Our method begins by employing a feature pyramid to gather coarse multi-scale features. Subsequently, it integrates a fine-grained feature extraction module at each level, utilizing atrous convolutions with varied dilation rates to refine multi-scale features, which markedly improves the information capture from widely varied object scales. Furthermore, an adaptive enhancement module is applied to the features of each level by employing an attention mechanism for feature fusion. This strategy concentrates on the features of critical scale, which significantly enhance the effectiveness of capturing essential feature information. Compared with the baseline method, namely, Rotated FasterRCNN, our method achieved an mAP of 74.21% ( 0.81%) on the DOTA-v1.0 dataset and an mAP of 84.90% (+9.2%) on the HRSC2016 dataset. These results validated the effectiveness and practicality of our method and demonstrated its significant application value in multi-scale remote sensing object detection tasks.

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Info-FPN: An Informative Feature Pyramid Network for object detection in remote sensing images

Cited by 33 publications

References 60 publications

A Survey of Small Object Detection Based on Deep Learning in Aerial Images

A Survey of Small Object Detection Based on Deep Learning in Aerial Images

A Review: Remote Sensing Image Object Detection Algorithm Based on Deep Learning

Object Detection in Remote Sensing Images Based on Adaptive Multi-Scale Feature Fusion Method

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