Fast semantic segmentation method for machine vision inspection based on a fewer-parameters atrous convolution neural network

Huang, Jian; Liu, Guixiong; He, Binyuan

doi:10.1371/journal.pone.0246093

Cited by 3 publications

(2 citation statements)

References 22 publications

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“…With the continuous progress of science and technology, computerized testing has developed rapidly, and the categories of computerized products are getting richer and richer, and the quality requirements for testing are getting higher and higher [1]. In machine management, the accuracy of the detection results is very critical [2]. Due to the influence of factors such as the failure of traditional detection equipment or operational errors in the detection process, the detection results have some errors [3].…”

Section: Introductionmentioning

confidence: 99%

Strategies for improving the detection accuracy of computerized machine vision considering spatial applications

Piao,

Song

2024

3C TIC

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In this paper, strategies in image preprocessing, hardware composition and detection methods are considered to improve computerized machine vision detection accuracy. First, image preprocessing and image enhancement are performed to improve the quality of the input image. Second, the hardware composition of the computer vision online inspection system is optimized by focusing on the light source selection and the performance of the image acquisition card in spatial applications. Combined with spatial application calculations, methods such as frequency domain method and Canny operator are used in order to improve the accuracy of machine vision detection. Finally, in the same test environment, the machine vision detection requires only 400MB and the detection accuracy ranges from 85.13% to 99.42%. With these comprehensive strategies, this paper provides a comprehensive and effective approach for computerized machine vision detection in spatial applications to improve detection accuracy and meet demanding application scenarios.

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Section: Introductionmentioning

confidence: 99%

Strategies for improving the detection accuracy of computerized machine vision considering spatial applications

Piao,

Song

2024

3C TIC

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“…In addition, due to the effectiveness in modeling multi-scale contextual information, Atrous Spatial Pyramid Pooling (ASPP) or its variants have showed promising performance in semantic segmentation [26][27][28], stereo matching [29][30][31] and object detection [32][33][34]. We also add ASPP on the final layer of our SPNet to further enhance the learning capability of our network.…”

Section: Introductionmentioning

confidence: 99%

SPNet: Structure preserving network for depth completion

Luo

Fan

et al. 2023

PLoS ONE

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Depth completion aims to predict a dense depth map from a sparse one. Benefiting from the powerful ability of convolutional neural networks, recent depth completion methods have achieved remarkable performance. However, it is still a challenging problem to well preserve accurate depth structures, such as tiny structures and object boundaries. To tackle this problem, we propose a structure preserving network (SPNet) in this paper. Firstly, an efficient multi-scale gradient extractor (MSGE) is proposed to extract useful multi-scale gradient images, which contain rich structural information that is helpful in recovering accurate depth. The MSGE is constructed based on the proposed semi-fixed depthwise separable convolution. Meanwhile, we adopt a stable gradient MAE loss (LGMAE) to provide additional depth gradient constrain for better structure reconstruction. Moreover, a multi-level feature fusion module (MFFM) is proposed to adaptively fuse the spatial details from low-level encoder and the semantic information from high-level decoder, which will incorporate more structural details into the depth modality. As demonstrated by experiments on NYUv2 and KITTI datasets, our method outperforms some state-of-the-art methods in terms of both quantitative and quantitative evaluations.

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Accuracy is not enough: a heterogeneous ensemble model versus FGSM attack

Elsheikh,

Mohamed,

Abou-Taleb

et al. 2024

Complex Intell. Syst.

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In this paper, based on facial landmark approaches, the possible vulnerability of ensemble algorithms to the FGSM attack has been assessed using three commonly used models: convolutional neural network-based antialiasing (A_CNN), Xc_Deep2-based DeepLab v2, and SqueezeNet (Squ_Net)-based Fire modules. Firstly, the three individual deep learning classifier-based Facial Emotion Recognition (FER) classifications have been developed; the predictions from all three classifiers are then merged using majority voting to develop the HEM_Net-based ensemble model. Following that, an in-depth investigation of their performance in the case of attack-free has been carried out in terms of the Jaccard coefficient, accuracy, precision, recall, F1 score, and specificity. When applied to three benchmark datasets, the ensemble-based method (HEM_Net) significantly outperforms in terms of precision and reliability while also decreasing the dimensionality of the input data, with an accuracy of 99.3%, 87%, and 99% for the Extended Cohn-Kanade (CK+), Real-world Affective Face (RafD), and Japanese female facial expressions (Jaffee) data, respectively. Further, a comprehensive analysis of the drop in performance of every model affected by the FGSM attack is carried out over a range of epsilon values (the perturbation parameter). The results from the experiments show that the advised HEM_Net model accuracy declined drastically by 59.72% for CK + data, 42.53% for RafD images, and 48.49% for the Jaffee dataset when the perturbation increased from A to E (attack levels). This demonstrated that a successful Fast Gradient Sign Method (FGSM) can significantly reduce the prediction performance of all individual classifiers with an increase in attack levels. However, due to the majority voting, the proposed HEM_Net model could improve its robustness against FGSM attacks, indicating that the ensemble can lessen deception by FGSM adversarial instances. This generally holds even as the perturbation level of the FGSM attack increases.

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Fast semantic segmentation method for machine vision inspection based on a fewer-parameters atrous convolution neural network

Cited by 3 publications

References 22 publications

Strategies for improving the detection accuracy of computerized machine vision considering spatial applications

Strategies for improving the detection accuracy of computerized machine vision considering spatial applications

SPNet: Structure preserving network for depth completion

Accuracy is not enough: a heterogeneous ensemble model versus FGSM attack

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