MRFU-Net: A Multiple Receptive Field U-Net for Environmental Microorganism Image Segmentation

Li, Chen; Jing-hua, Zhang; Zhao, Xin; Kulwa, Frank; Xu, Hao; Li, Hong

doi:10.1007/978-3-030-49666-1_3

Cited by 5 publications

(14 citation statements)

References 21 publications

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“…In light of the characteristics of defect detection tasks, the 3 × 3 depth-wise convolutional layers in the right branch of both basic unit ( Figure 2 a) and spatial down-sampling (2×) unit ( Figure 2 b) are replaced by 2 consecutive 1-bit 3 × 3 convolutions while the left branch of down-sampling unit ( Figure 2 b) is substituted by 1-bit 5 × 5 convolutions, in order to enjoy a larger receptive field to detect defects of various scales. Applying multiple 3 × 3 convolutions in sequence to enjoy a larger reception field is a common and efficient idea in object segmentation [ 38 , 39 , 40 ] to save parameters in the meantime. Correspondingly, the kernel size of the first convolution layer is defined to be 9 × 9 as well.…”

Section: The Proposed Methodsmentioning

confidence: 99%

Binary Neural Network for Automated Visual Surface Defect Detection

Liu

Zhang

et al. 2021

Sensors

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As is well-known, defects precisely affect the lives and functions of the machines in which they occur, and even cause potentially catastrophic casualties. Therefore, quality assessment before mounting is an indispensable requirement for factories. Apart from the recognition accuracy, current networks suffer from excessive computing complexity, making it of great difficulty to deploy in the manufacturing process. To address these issues, this paper introduces binary networks into the area of surface defect detection for the first time, for the reason that binary networks prohibitively constrain weight and activation to +1 and −1. The proposed Bi-ShuffleNet and U-BiNet utilize binary convolution layers and activations in low bitwidth, in order to reach comparable performances while incurring much less computational cost. Extensive experiments are conducted on real-life NEU and Magnetic Tile datasets, revealing the least OPs required and little accuracy decline. When classifying the defects, Bi-ShuffleNet yields comparable results to counterpart networks, with at least 2× inference complexity reduction. Defect segmentation results indicate similar observations. Some network design rules in defect detection and binary networks are also summarized in this paper.

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Section: The Proposed Methodsmentioning

confidence: 99%

Binary Neural Network for Automated Visual Surface Defect Detection

Liu

Zhang

et al. 2021

Sensors

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“…AI has several important subdomains, such as Machine learning (ML), computer vision, natural language processing, etc. Among them, ML technology has been widely used in the MIA task, such as Environmental Microorganism (EM) segmentation [82,167], Herpesvirus detection [33], and Tuberculosis Bacilli (TB) classification [121,103].…”

Section: Artificial Intelligence Methods For Microorganism Image Anal...mentioning

confidence: 99%

“…However, there are also many harmful microorganisms, such as Mycobacterium tuberculosis can lead to disease and death [43], and the novel coronavirus disease 2019 (COVID-19) constitutes a public health emergency globally [112]. Therefore, microorganism research plays a vital role in pollution monitoring, environmental management, medical diagnosis, agriculture, and food production [70,80], and the analysis of microorganisms is the essential step for related researches and applications [82]. Fig.…”

Section: Introductionmentioning

confidence: 99%

Applications of Artificial Neural Networks in Microorganism Image Analysis: A Comprehensive Review from Conventional Multilayer Perceptron to Popular Convolutional Neural Network and Potential Visual Transformer

Zhang¹,

Li²,

Yin³

et al. 2021

Preprint

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Microorganisms are widely distributed in the human daily living environment. They play an essential role in environmental pollution control, disease prevention and treatment, and food and drug production. The identification, counting, and detection are the basic steps for making full use of different microorganisms. However, the conventional analysis methods are expensive, laborious, and time-consuming. To overcome these limitations, artificial neural networks are applied for microorganism image analysis. We conduct this review to understand the development process of microorganism image analysis based on artificial neural networks. In this review, the background and motivation are introduced first. Then, the development of artificial neural networks and representative networks are introduced. After that, the papers related to microorganism image analysis based on classical and deep neural networks are reviewed from the perspectives of different tasks. In the end, the methodology analysis and potential direction are discussed.

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“…The remaining five are our EMDS series. The seven databases are NMCR [6], CECC [7], EMDS-1 [8][9][10], EMDS-2 [8][9][10][11][12], EMDS-3 [1,13,14], EMDS-4 [15][16][17][18] and EMDS-5 [19,20]. Environmental Microorganism Data Set Fifth Version (EMDS-5) has been made available to other researchers as an open source dataset.…”

Section: Contributionmentioning

confidence: 99%

“…lost in each convolution, so cropping is necessary. In the last layer, each 64-component feature vector is mapped to the desired number of classes using 1 × 1 convolution [25].…”

Section: Plos Onementioning

confidence: 99%

EMDS-5: Environmental Microorganism image dataset Fifth Version for multiple image analysis tasks

Yao

Jing-hua

et al. 2021

PLoS ONE

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Environmental Microorganism Data Set Fifth Version (EMDS-5) is a microscopic image dataset including original Environmental Microorganism (EM) images and two sets of Ground Truth (GT) images. The GT image sets include a single-object GT image set and a multi-object GT image set. EMDS-5 has 21 types of EMs, each of which contains 20 original EM images, 20 single-object GT images and 20 multi-object GT images. EMDS-5 can realize to evaluate image preprocessing, image segmentation, feature extraction, image classification and image retrieval functions. In order to prove the effectiveness of EMDS-5, for each function, we select the most representative algorithms and price indicators for testing and evaluation. The image preprocessing functions contain two parts: image denoising and image edge detection. Image denoising uses nine kinds of filters to denoise 13 kinds of noises, respectively. In the aspect of edge detection, six edge detection operators are used to detect the edges of the images, and two evaluation indicators, peak-signal to noise ratio and mean structural similarity, are used for evaluation. Image segmentation includes single-object image segmentation and multi-object image segmentation. Six methods are used for single-object image segmentation, while k-means and U-net are used for multi-object segmentation. We extract nine features from the images in EMDS-5 and use the Support Vector Machine (SVM) classifier for testing. In terms of image classification, we select the VGG16 feature to test SVM, k-Nearest Neighbors, Random Forests. We test two types of retrieval approaches: texture feature retrieval and deep learning feature retrieval. We select the last layer of features of VGG16 network and ResNet50 network as feature vectors. We use mean average precision as the evaluation index for retrieval. EMDS-5 is available at the URL:https://github.com/NEUZihan/EMDS-5.git.

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MRFU-Net: A Multiple Receptive Field U-Net for Environmental Microorganism Image Segmentation

Cited by 5 publications

References 21 publications

Binary Neural Network for Automated Visual Surface Defect Detection

Binary Neural Network for Automated Visual Surface Defect Detection

Applications of Artificial Neural Networks in Microorganism Image Analysis: A Comprehensive Review from Conventional Multilayer Perceptron to Popular Convolutional Neural Network and Potential Visual Transformer

EMDS-5: Environmental Microorganism image dataset Fifth Version for multiple image analysis tasks

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