Adaptive Feature Pyramid Networks for Object Detection

Wang, Chengyang; Zhong, Chongquan

doi:10.1109/access.2021.3100369

Cited by 44 publications

(16 citation statements)

References 27 publications

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“…The proposed method outperformed the other related deep learning methods of Faster R-CNN with Feature Pyramid Network (FPN), worthy of comparison to experienced pathologists with a 10-year of experience on an independent dataset. The findings are consistent with the study by several researchers, which proposed the utilization of the Faster R-CNN method for the detection of cervical cancer cells [34,35]. Besides, Tang et al [36] proposed the comparison detector based on a proposal-based detection framework which often consists of a backbone network for feature extraction, an RPN for generating proposals and a head for the proposed classification and bounding box regression.…”

Section: Detection Methods Based On Cells/pap Smear Imagessupporting

confidence: 88%

Improvement method for cervical cancer detection: A comparative analysis

Alias¹,

Mustafa²,

Jamlos³

et al. 2021

Oncology Research

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Cervical cancer is a prevalent and deadly cancer that affects women all over the world. It affects about 0.5 million women anually and results in over 0.3 million fatalities. Diagnosis of this cancer was previously done manually, which could result in false positives or negatives. The researchers are still contemplating how to detect cervical cancer automatically and how to evaluate Pap smear images. Hence, this paper has reviewed several detection methods from the previous researches that has been done before. This paper reviews pre-processing, detection method framework for nucleus detection, and analysis performance of the method selected. There are four methods based on a reviewed technique from previous studies that have been running through the experimental procedure using Matlab, and the dataset used is established Herlev Dataset. The results show that the highest performance assessment metric values obtain from Method 1: Thresholding and Trace region boundaries in a binary image with the values of precision 1.0, sensitivity 98.77%, specificity 98.76%, accuracy 98.77% and PSNR 25.74% for a single type of cell. Meanwhile, the average values of precision were 0.99, sensitivity 90.71%, specificity 96.55%, accuracy 92.91% and PSNR 16.22%. The experimental results are then compared to the existing methods from previous studies. They show that the improvement method is able to detect the nucleus of the cell with higher performance assessment values. On the other hand, the majority of current approaches can be used with either a single or a large number of cervical cancer smear images. This study might persuade other researchers to recognize the value of some of the existing detection techniques and offer a strong approach for developing and implementing new solutions.

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Section: Detection Methods Based On Cells/pap Smear Imagessupporting

confidence: 88%

Improvement method for cervical cancer detection: A comparative analysis

Alias¹,

Mustafa²,

Jamlos³

et al. 2021

Oncology Research

View full text Add to dashboard Cite

show abstract

“…Therefore, it is necessary to focus on the problem of corner region characterisation in dense distribution prediction tasks. For this purpose, an efficient centre of visualisation EVC module [27] is introduced in this paper to improve the feature capture rate in local corner regions of the input image.…”

Section: Proposed Methodsmentioning

confidence: 99%

Multiscale feature cross‐layer fusion remote sensing target detection method

Lin

Zhang

Huang

2023

IET Signal Processing

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Target detection based on remotely sensed images, which has recently attracted much attention, is a fundamental but challenging task. In remote sensing images, the problem of difficult recognition of small targets or targets with a large aspect ratio arises because the targets have the characteristics of small proportion, dense distribution, and multidirectionality. To address the above problems, this article proposes an improved multiscale feature cross‐layer fusion remote sensing target detector based on YOLOv5. First, this method introduces the circular smooth label technique, using YOLOv5 as a rotation detector to solve the angular boundary condition and angle prediction problem for large aspect ratio targets. Second, the explicit visual centre module is introduced to solve the problem of missed detection in target‐dense distribution tasks. Finally, a multiscale feature cross‐layer fusion structure (S‐160) is proposed based on YOLOv5, which improves the detection accuracy of each scale target by fusing shallow and deep feature information and introduces new large‐scale features for small target detection to solve the problem that ultrasmall targets in remote sensing images cannot be recognised. Our experiments were conducted on three public remote sensing datasets, DOTA, DIOR‐R, and HRSC2016, and the average accuracy (mAP) on the datasets was 76.50%, 70.34%, and 97.68%, respectively, demonstrating the substantial detection performance of the proposed method.

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“…On the other hand, in the top-down method, the higher resolution features are more spatially distant but semantically stronger. Each side connection combines feature maps of the same spatial size from the bottom-up and top-down paths [ 50 ].…”

Section: Methodsmentioning

confidence: 99%

Sea Mine Detection Framework Using YOLO, SSD and EfficientDet Deep Learning Models

Munteanu

Diana

Zamfir

et al. 2022

Sensors

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In the context of new geopolitical tensions due to the current armed conflicts, safety in terms of navigation has been threatened due to the large number of sea mines placed, in particular, within the sea conflict areas. Additionally, since a large number of mines have recently been reported to have drifted into the territories of the Black Sea countries such as Romania, Bulgaria Georgia and Turkey, which have intense commercial and tourism activities in their coastal areas, the safety of those economic activities is threatened by possible accidents that may occur due to the above-mentioned situation. The use of deep learning in a military operation is widespread, especially for combating drones and other killer robots. Therefore, the present research addresses the detection of floating and underwater sea mines using images recorded from cameras (taken from drones, submarines, ships and boats). Due to the low number of sea mine images, the current research used both an augmentation technique and synthetic image generation (by overlapping images with different types of mines over water backgrounds), and two datasets were built (for floating mines and for underwater mines). Three deep learning models, respectively, YOLOv5, SSD and EfficientDet (YOLOv5 and SSD for floating mines and YOLOv5 and EfficientDet for underwater mines), were trained and compared. In the context of using three algorithm models, YOLO, SSD and EfficientDet, the new generated system revealed high accuracy in object recognition, namely the detection of floating and anchored mines. Moreover, tests carried out on portable computing equipment, such as Raspberry Pi, illustrated the possibility of including such an application for real-time scenarios, with the time of 2 s per frame being improved if devices use high-performance cameras.

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Adaptive Feature Pyramid Networks for Object Detection

Cited by 44 publications

References 27 publications

Improvement method for cervical cancer detection: A comparative analysis

Improvement method for cervical cancer detection: A comparative analysis

Multiscale feature cross‐layer fusion remote sensing target detection method

Sea Mine Detection Framework Using YOLO, SSD and EfficientDet Deep Learning Models

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