Rapid Detection Algorithm for Small Objects Based on Receptive Field Block

Currently, analyzing the microscopic image of cotton fiber cross-section is the most accurate and effective way to measure its grade of maturity and then evaluate the quality of cotton samples. However, existing methods cannot extract the edge of the cross-section intact, which will affect the measurement accuracy of maturity grade. In this paper, a new edge detection algorithm that is based on the RCF convolutional neural network (CNN) is proposed. For the microscopic image dataset of the cotton fiber cross-section constructed in this paper, the original RCF was firstly used to extract the edge of the cotton fiber cross-section in the image. After analyzing the output images of RCF in each convolution stage, the following two conclusions are drawn: (1) the shallow layers contain a lot of important edge information of the cotton fiber cross-section; (2) because the size of the cotton fiber cross-section in the image is relatively small and the receptive field of the convolutional layer gradually increases with the deepening of the number of layers, the edge information detected by the deeper layers becomes increasingly coarse. In view of the above two points, the following improvements are proposed in this paper: (1) modify the network supervision model and loss calculation structure; (2) the dilated convolution in the deeper layers is removed; therefore, the receptive field in the deeper layers is reduced to adapt to the detection of small objects. The experimental results show that the proposed method can effectively improve the accuracy of edge extraction of cotton fiber cross-section.

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A Face Detection Algorithm Based on Two Information Flow Block and Retinal Receptive Field Block

IEEE Access ◽

10.1109/access.2020.2973071 ◽

2020 ◽

Vol 8 ◽

pp. 30682-30691 ◽

Cited By ~ 2

Author(s):

Shaoqi Hou ◽

Ye Li ◽

Yixi Pan ◽

Xiaoyu Yang ◽

Guangqiang Yin

Keyword(s):

Receptive Field ◽

Face Detection ◽

Information Flow ◽

Detection Algorithm

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Application of the TRUFAS detection algorithm to the first two runs of CoRoT

Proceedings of the International Astronomical Union ◽

10.1017/s1743921308026689 ◽

2008 ◽

Vol 4 (S253) ◽

pp. 374-377

Author(s):

Clara Régulo ◽

Jose M. Almenara ◽

Hans J. Deeg

Keyword(s):

Fast Algorithm ◽

Rapid Detection ◽

Detection Algorithm ◽

Space Mission ◽

Eclipsing Binaries ◽

Long Run ◽

Planetary Transits

AbstractTRUFAS is a wavelet-based algorithm developed for the rapid detection of planetary transits in the frame of the COROT space mission. We present the application of this algorithm to the first two observing fields of CoRoT data. In these, CoRoT has observed a total of about 20000 stars. The first CoRoT observing run, IRa01, covers 2 months, February and March 2007, followed by the 5-months long run LRc01. TRUFAS is a very fast algorithm delivering reliable detections. Here we show the results when TRUFAS was applied to these first two sets of data. In the first run, IRa01, TRUFAS found 10 planet candidates and 143 eclipsing binaries and in the LRc01 10 planet candidates and 124 binaries, with a processing that lasted only one night.

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A rapid detection algorithm of corrupted data in cloud storage

Journal of Parallel and Distributed Computing ◽

10.1016/j.jpdc.2017.08.004 ◽

2018 ◽

Vol 111 ◽

pp. 115-125 ◽

Cited By ~ 3

Author(s):

Guangwei Xu ◽

Zhifeng Sun ◽

Cairong Yan ◽

Yanglan Gan

Keyword(s):

Rapid Detection ◽

Cloud Storage ◽

Detection Algorithm

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Adaptive Pneumonia Detection Algorithm based on Deep Learning

Journal of Imaging Science and Technology ◽

10.2352/j.imagingsci.technol.2022.66.1.010402 ◽

2021 ◽

Author(s):

Yuxin Wu ◽

Qiang Li ◽

I-Chi Wang

Keyword(s):

Feature Extraction ◽

Receptive Field ◽

Receptive Fields ◽

Detection Algorithm ◽

Average Precision ◽

Data Set ◽

Radiological Society ◽

Kernel Convolution ◽

Minimal Reduction ◽

Detection Speed

General convolutional neural networks are unable to automatically adjust their receptive fields for the detection of pneumonia lesion regions. This study, therefore, proposes a pneumonia detection algorithm with automatic receptive field adjustment. This algorithm is a modified form of RetinaNet with selective kernel convolution incorporated into the feature extraction network ResNet. The resulting SK-ResNet automatically adjusts the size of the receptive field. The convolutional neural network can then generate prediction bounds with sizes corresponding to those of the targets. In addition, the authors aggregated the detection results with SK-ResNet50 and SK-ResNet152 for the feature extraction network to further enhancing average precision (AP). With a data set provided by the Radiological Society of North America, the proposed algorithm with SK-ResNet50 as the feature extraction network resulted in AP 50 that was 1.5% higher than that returned by RetinaNet. The number of images processed per second differed by only 0.45, which indicated that AP was increased while detection speed was maintained. After the detection results with the SK-ResNet50 and SK-ResNet152 as the feature extraction network were combined, AP 50 increased by 3.3% compared to the RetinaNet algorithm. The experimental results show that the proposed algorithm is effective at automatically adjusting the size of the receptive field based on the size of the target, as well as increasing AP with minimal reduction in speed.

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Research on Rapid Detection Algorithm Based on Property Choosing Tree

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.505.386 ◽

2012 ◽

Vol 505 ◽

pp. 386-392

Author(s):

Neng Shan Feng ◽

Zhong Ming Yang

Keyword(s):

Decision Tree ◽

Rapid Detection ◽

Detection Algorithm ◽

Construction Method ◽

The State ◽

Transport Layer ◽

Network Protocol ◽

Protocol Stack ◽

Link Layer ◽

Network Layer

The construction method used by detection engine Snort-NG based on ID3 decision tree has the problem of excessive memory occupancy. The idea that the test properties are chosen according to the gradation of rule property in network protocol stack was presented in this paper; that is, the property of link layer first determined, and then network layer and transport layer. The atomicity of the value of these properties were preserved and the values of these properties were treated as a whole. The results of experiment showed that the occupancy of memory was much less in the state of non-trivial property being very common with this approach.

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SharpGAN: Dynamic Scene Deblurring Method for Smart Ship Based on Receptive Field Block and Generative Adversarial Networks

Sensors ◽

10.3390/s21113641 ◽

2021 ◽

Vol 21 (11) ◽

pp. 3641

Author(s):

Hui Feng ◽

Jundong Guo ◽

Haixiang Xu ◽

Shuzhi Sam Ge

Keyword(s):

Receptive Field ◽

Evaluation Criteria ◽

Objective Evaluation ◽

Detection Algorithm ◽

Image Features ◽

Motion Blur ◽

Generative Adversarial Networks ◽

Generative Adversarial Network ◽

Adversarial Network ◽

Blurred Image

Complex marine environment has an adverse effect on the object detection algorithm based on the vision sensor for the smart ship sailing at sea. In order to eliminate the motion blur in the images during the navigation of the smart ship and ensure safety, we propose SharpGAN, a new image deblurring method based on the generative adversarial network (GAN). First of all, we introduce the receptive field block net (RFBNet) to the deblurring network to enhance the network’s ability to extract blurred image features. Secondly, we propose a feature loss that combines different levels of image features to guide the network to perform higher-quality deblurring and improve the feature similarity between the restored images and the sharp images. Besides, we use the lightweight RFB-s module to significantly improve the real-time performance of the deblurring network. Compared with the existing deblurring methods, the proposed method not only has better deblurring performance in subjective visual effects and objective evaluation criteria, but also has higher deblurring efficiency. Finally, the experimental results reveal that the SharpGAN has a high correlation with the deblurring methods based on the physical model.

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Search mode for rapid detection of virus particles

Proceedings, annual meeting, Electron Microscopy Society of America ◽

10.1017/s0424820100085733 ◽

1991 ◽

Vol 49 ◽

pp. 286-287

Author(s):

O. E. Bradfute

Keyword(s):

Electron Microscopy ◽

Rapid Detection ◽

Plant Virus ◽

Low Dose ◽

Search Mode ◽

Virus Diseases ◽

Virus Particles ◽

Focus Image ◽

Time Required ◽

Rapid Switching

Electron microscopy is frequently used in preliminary diagnosis of plant virus diseases by surveying negatively stained preparations of crude extracts of leaf samples. A major limitation of this method is the time required to survey grids when the concentration of virus particles (VPs) is low. A rapid survey of grids for VPs is reported here; the method employs a low magnification, out-of-focus Search Mode similar to that used for low dose electron microscopy of radiation sensitive specimens. A higher magnification, in-focus Confirm Mode is used to photograph or confirm the detection of VPs. Setting up the Search Mode by obtaining an out-of-focus image of the specimen in diffraction (K. H. Downing and W. Chiu, private communications) and pre-aligning the image in Search Mode with the image in Confirm Mode facilitates rapid switching between Modes.

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Identification of hepatitis a virus in cell cultures by solid phase immune electron microscopy

Proceedings, annual meeting, Electron Microscopy Society of America ◽

10.1017/s0424820100142815 ◽

1986 ◽

Vol 44 ◽

pp. 238-239

Author(s):

C.D. Humphrey ◽

T.L. Cromeans ◽

E.H. Cook ◽

D.W. Bradley

Keyword(s):

Electron Microscopy ◽

Liquid Phase ◽

Cell Cultures ◽

Rapid Detection ◽

Hepatitis A ◽

Hepatitis A Virus ◽

Solid Phase ◽

Immune Electron Microscopy ◽

Detection And Identification

There is a variety of methods available for the rapid detection and identification of viruses by electron microscopy as described in several reviews. The predominant techniques are classified as direct electron microscopy (DEM), immune electron microscopy (IEM), liquid phase immune electron microscopy (LPIEM) and solid phase immune electron microscopy (SPIEM). Each technique has inherent strengths and weaknesses. However, in recent years, the most progress for identifying viruses has been realized by the utilization of SPIEM.

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