Automated detection of parasitized Cadra cautella eggs by 
Trichogramma bourarachae using machine vision

El-Faki, M. S.; Song, Yuqi; Zhang, Naiqian; El-Shafie, Hamadttu A. F.; Pan, Xuebiao

doi:10.25165/j.ijabe.20181103.2895

Cited by 8 publications

(6 citation statements)

References 10 publications

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“…In this paper, computing speed is not a priority, because glass defect detection does not require real-time detection on the production line. The detection accuracy is relatively important, as it directly bears on process adjustment [8,9]. Each process adjustment needs a lot of resources.…”

Section: Glass Defect Detection Based On Faster-rcnnmentioning

confidence: 99%

End Image Defect Detection of Float Glass Based on Faster Region-Based Convolutional Neural Network

Jin¹,

Xu²,

Tong³

et al. 2020

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The float glass contains various defects for reasons of raw materials and production process. These defects can be observed on the end images of the glass. Since the defects are correlated with specific links of the production process, it is possible to discover the process problems by identifying the location and type of defects in end images. Based on faster region-based convolutional neural network (Faster RCNN), this paper proposes a deep learning method that improves the feature extraction network, and adds a Laplacian convolutional layer to preprocess the end images. Considering the defect features in end images, the anchor box size was adjusted to speed up the training. Besides, the lack of generalizability induced by small dataset was solved through data enhancement. With improved VGG16 as the feature extraction layer, a glass defect detection model was established, whose generalizability was improved through transfer learning. The experimental results show that the proposed model achieved a mean detection accuracy of 94% on actual test set, meeting the requirements for actual use in factories.

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Section: Glass Defect Detection Based On Faster-rcnnmentioning

confidence: 99%

End Image Defect Detection of Float Glass Based on Faster Region-Based Convolutional Neural Network

Jin¹,

Xu²,

Tong³

et al. 2020

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“…The SGD supports the momentum parameter and learns the attenuation rate. Instead of computing the loss on all training data, the SGD algorithm calculates the loss based on a randomly selected part of the data [27,28]. This speeds up the parameter update in each iteration.…”

Section: Figure 6 Structure Of Dilated U-netmentioning

confidence: 99%

A Deep Learning Model for Striae Identification in End Images of Float Glass

Jin¹,

Xu²,

Tong³

et al. 2020

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For float glass, there is a correlation between the striae in end image and the manufacturing process. If clearly understood, the correlation helps to optimize and fine-tune the manufacturing process of float glass. This paper attempts to extract the striae from the end image of float glass with deep learning (DL) neural network (NN). For this purpose, an image segmentation model was established based on improved U-Net, a fully convolutional network (FCN), and used to accurately divide the glass liquid on the end image into different layers. Firstly, the improved U-Net model was constructed to extract the striae from each liquid layer on the end image. Next, the activation function and convolutional mode of the improved U-Net model were optimized to enhance the segmentation accuracy and shorten the training/prediction time. Finally, the proposed model was tested on the float glass production line of Hebei CSG Glass Co., Ltd. The test results show that our model achieved an accuracy of 94%. The research findings lay a solid basis for striae identification on end image of float glass, and provide guidance for optimization and fine-tuning of float glass manufacturing process.

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“…With the rapid development of machine learning and artificial intelligence technology, the accuracy of pest detection technology based on deep learning in actual agricultural scenes has exceeded that of traditional agricultural experts [ 10 , 11 ], and the calculation and analysis of efficiency are high, which greatly widen the possibility of application of the pest detection method based on deep learning technology [ 12 , 13 ]. However, due to the incomplete dataset and the influence of the network's structure, the depth network model has the overfitting problem, resulting in the low accuracy of image recognition, which cannot meet the needs of efficient analysis of actual agricultural work scenes.…”

Section: Introductionmentioning

confidence: 99%

Improved CNN Method for Crop Pest Identification Based on Transfer Learning

Liu

Zhang

Gao

et al. 2022

Computational Intelligence and Neuroscience

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Timely treatment and elimination of diseases and pests can effectively improve the yield and quality of crops, but the current identification methods are difficult to achieve efficient and accurate research and analysis of diseases and pests. To solve this problem, this study proposes a crop pest identification method based on a multilayer network model. First, the method provides a reliable sample dataset for the recognition model through image data enhancement and other operations; the corresponding pest image recognition and analysis model is constructed based on VGG16 and Inception-ResNet-v2 transfer learning network to ensure the completeness of the recognition and analysis model; then, using the idea of an integrated algorithm, the two improved CNN series pest image recognition and analysis models are effectively fused to improve the accuracy of the model for crop pest recognition and classification. The simulation analysis is realized based on the IDADP dataset. The experimental results show that the accuracy of the proposed method for pest identification is 97.71%, which improves the poor identification effect of the current method.

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Automated detection of parasitized Cadra cautella eggs by Trichogramma bourarachae using machine vision

Cited by 8 publications

References 10 publications

End Image Defect Detection of Float Glass Based on Faster Region-Based Convolutional Neural Network

End Image Defect Detection of Float Glass Based on Faster Region-Based Convolutional Neural Network

A Deep Learning Model for Striae Identification in End Images of Float Glass

Improved CNN Method for Crop Pest Identification Based on Transfer Learning

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