SV-FPN: Small Object Feature Enhancement and Variance-Guided RoI Fusion for Feature Pyramid Networks

Yang, Qianhui; Zhang, Changlun; Wang, Hengyou; He, Qing; Huo, Lianzhi

doi:10.3390/electronics11132028

Cited by 5 publications

(3 citation statements)

References 35 publications

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“…An FPN is a multiscale pyramid network that detects objects of varying sizes in images by using feature maps of varying resolutions [ 84 ]. An FPN has been used to recognize and segment small objects in aerial photos and to segment buildings in satellite images [ 85 , 86 , 87 ].…”

Section: Literature Reviewmentioning

confidence: 99%

YOLOv5-FPN: A Robust Framework for Multi-Sized Cell Counting in Fluorescence Images

et al. 2023

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Cell counting in fluorescence microscopy is an essential task in biomedical research for analyzing cellular dynamics and studying disease progression. Traditional methods for cell counting involve manual counting or threshold-based segmentation, which are time-consuming and prone to human error. Recently, deep learning-based object detection methods have shown promising results in automating cell counting tasks. However, the existing methods mainly focus on segmentation-based techniques that require a large amount of labeled data and extensive computational resources. In this paper, we propose a novel approach to detect and count multiple-size cells in a fluorescence image slide using You Only Look Once version 5 (YOLOv5) with a feature pyramid network (FPN). Our proposed method can efficiently detect multiple cells with different sizes in a single image, eliminating the need for pixel-level segmentation. We show that our method outperforms state-of-the-art segmentation-based approaches in terms of accuracy and computational efficiency. The experimental results on publicly available datasets demonstrate that our proposed approach achieves an average precision of 0.8 and a processing time of 43.9 ms per image. Our approach addresses the research gap in the literature by providing a more efficient and accurate method for cell counting in fluorescence microscopy that requires less computational resources and labeled data.

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Section: Literature Reviewmentioning

confidence: 99%

YOLOv5-FPN: A Robust Framework for Multi-Sized Cell Counting in Fluorescence Images

et al. 2023

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“…FPN [18] is an important part of the Mask R-CNN in order to improve the robustness of Mask R-CNN for detecting targets of different sizes. FPN adopts a multi-scale feature fusion method, which can significantly improve the scale robustness of feature expression without greatly increasing the amount of computation [19].…”

Section: Fpnmentioning

confidence: 99%

“…First,A 4 is sent into the mixed hole convolution, which is composed of multi-path dilated convolution layers with different rates to ensure that the final receiving field fully covers the entire area. The rate is set to, rate = [3,6,12,18]. Use a different rate for each layer, and add a GroupNorm operation after each layer.…”

Section: Scbam-fpn Architecturementioning

confidence: 99%

Serial Document Layout Analysis Method Based on Improved Mask R-CNN Architecture

Zhao

Zhang

2023

Preprint

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Document Layout Analysis plays an important part in understanding the content of the document and extracting OCR text. Existing methods rely primarily on parallel analytical and identification techniques. Based on the analysis of the advantages and disadvantages of parallel methods, this article proposes a Serial Classification Recognition Method based on the improved architecture of the Mask R-CNN. To be specific, an improved feature pyramid model SCBAM-FPN is proposed, which utilizes Spatial Attention Module and Channel Attention Module as well as Atrous Convolution to reduce the loss of local information in feature maps and expand the receptive field, thereby increasing the performance of FPN express ability. Experiments show that using SCBAM-FPN to replace the original feature pyramid network in Mask R-CNN improves the detection performance of Mask R-CNN network for multi-scale targets. In addition, in order to improve the accuracy of segmentation, a Mask edge correction algorithm is proposed, which combines the FCN semantic segmentation results with edge superpixel information and uses the Mask-IoU mechanism to perform edge corrections on Masks with scores of less than 90%. The serial classification recognition method utilizes the improved Mask R-CNN architecture to perform Document Layout Analysis. Firstly, the document image is recognized and the table is segmented. Secondly, the image after the split table is used to identify and segment the illustrations. Finally, identify the title and paragraph of the image after removing the table and the illustration. Although the serial classification and recognition method has a certain degree of decline in speed, this method solves the problem of competition among multiple attributes and improves the accuracy of recognition compared with existing methods. In this article, experiments that were conducted on three datasets show that the proposed method surpasses existing methods in terms of accuracy and efficiency.

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Distribution-Aware Loss for Lesions Detection Using White-Light Endoscopy in Colorectal Region

Sun,

Zhao,

Zhang

et al. 2024

IEEE Access

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SV-FPN: Small Object Feature Enhancement and Variance-Guided RoI Fusion for Feature Pyramid Networks

Cited by 5 publications

References 35 publications

YOLOv5-FPN: A Robust Framework for Multi-Sized Cell Counting in Fluorescence Images

YOLOv5-FPN: A Robust Framework for Multi-Sized Cell Counting in Fluorescence Images

Serial Document Layout Analysis Method Based on Improved Mask R-CNN Architecture

Distribution-Aware Loss for Lesions Detection Using White-Light Endoscopy in Colorectal Region

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