A Hybrid Character Recognition Approach Using Fuzzy Logic and Stroke Bayesian Program Learning With Naïve Bayes in Industrial Environments

Liang, Xu; Wang, Yuxi; Li, Ruihui; Yang, Xiaonan; Li, Xiuxi

doi:10.1109/access.2020.3007487

Cited by 5 publications

(7 citation statements)

References 65 publications

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“…, level 5. In our fuzzy evaluation system, the membership function must be decided first, which is similar to our previous work [58,59]. Once the membership degree of every input has been determined, a set of rules, which are defined in the system, as shown in Table 6, can be used to explain the evaluation results.…”

Section: Results For Grading Of Forest Fire and Smokementioning

confidence: 99%

“…To assess the fire or smoke level, a fuzzy strategy is designed to weigh the variables CT, RS, and LT. This strategy is similar to that employed in our previous work [58,59]. The ambiguity Level = f (i) ∈ [0, 1,2,3,4,5] guides the evaluation of the possibility of fire or smoke level.…”

Section: Grading Methods Using Fuzzy Logicmentioning

confidence: 99%

“…The ambiguity term Level determines the final fire smoke level. The Mamdani model is applied as a reasoning engine [58,59], because it is suitable for capturing and coding expert-based knowledge.…”

Section: Grading Methods Using Fuzzy Logicmentioning

confidence: 99%

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A Collaborative Region Detection and Grading Framework for Forest Fire Smoke Using Weakly Supervised Fine Segmentation and Lightweight Faster-RCNN

Pan

Liang

2021

Forests

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Forest fires are serious disasters that affect countries all over the world. With the progress of image processing, numerous image-based surveillance systems for fires have been installed in forests. The rapid and accurate detection and grading of fire smoke can provide useful information, which helps humans to quickly control and reduce forest losses. Currently, convolutional neural networks (CNN) have yielded excellent performance in image recognition. Previous studies mostly paid attention to CNN-based image classification for fire detection. However, the research of CNN-based region detection and grading of fire is extremely scarce due to a challenging task which locates and segments fire regions using image-level annotations instead of inaccessible pixel-level labels. This paper presents a novel collaborative region detection and grading framework for fire smoke using a weakly supervised fine segmentation and a lightweight Faster R-CNN. The multi-task framework can simultaneously implement the early-stage alarm, region detection, classification, and grading of fire smoke. To provide an accurate segmentation on image-level, we propose the weakly supervised fine segmentation method, which consists of a segmentation network and a decision network. We aggregate image-level information, instead of expensive pixel-level labels, from all training images into the segmentation network, which simultaneously locates and segments fire smoke regions. To train the segmentation network using only image-level annotations, we propose a two-stage weakly supervised learning strategy, in which a novel weakly supervised loss is proposed to roughly detect the region of fire smoke, and a new region-refining segmentation algorithm is further used to accurately identify this region. The decision network incorporating a residual spatial attention module is utilized to predict the category of forest fire smoke. To reduce the complexity of the Faster R-CNN, we first introduced a knowledge distillation technique to compress the structure of this model. To grade forest fire smoke, we used a 3-input/1-output fuzzy system to evaluate the severity level. We evaluated the proposed approach using a developed fire smoke dataset, which included five different scenes varying by the fire smoke level. The proposed method exhibited competitive performance compared to state-of-the-art methods.

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Section: Results For Grading Of Forest Fire and Smokementioning

confidence: 99%

Section: Grading Methods Using Fuzzy Logicmentioning

confidence: 99%

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A Collaborative Region Detection and Grading Framework for Forest Fire Smoke Using Weakly Supervised Fine Segmentation and Lightweight Faster-RCNN

Pan

Liang

2021

Forests

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“…This paper proposed an architecture based on the improved LBP shallow deep convolution neural network, which integrates ILBP feature pre-processing to improve character recognition, FIGURE 14 Relevance between channel ratio and accuracy and two feature maps (FLBP and MLBP) were selected to preserve image details and remove image noise, respectively. In terms of the network architecture, two branch networks of different depths were constructed by importing the shallow and deep neural network architecture, as based on the lightweight design principle.…”

Section: Discussionmentioning

confidence: 99%

“…Handwritten characters can be extracted by LBP technology and recognized by KNN [13]. A hybrid method using fuzzy logic and naïve Bayes to learn strokes [14] has been proposed for character recognition. During character segmentation, characters are segmented by support vector machines and the three-feature fuzzy segmentation strategy of particle swarm optimization, and character classification is predicted by Monte Carlo Markov chain sampling.…”

Section: Introductionmentioning

confidence: 99%

ILBPSDNet: Based on improved local binary pattern shallow deep convolutional neural network for character recognition

Lee

Yang

2021

IET Image Processing

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This paper proposes an architecture based on the improved local binary pattern (LBP) shallow deep convolution neural network, which integrates hand-crafted feature preprocessing and the advantage of character learning in the supervised high-level function of CNN, in order to enhance its performance. This study introduced the information of scale space into the LBP to reduce the sensitivity to noise, and applied feature maps with two features, the maximum selection feature map (MLBP) and the first selection feature map (FLBP). The former selected the edge with the strongest intensity to reduce the influence of noise points, while the latter measured local binary features through the scale detection of an effective edge. In the network architecture design, according to the differences of input features, networks of different depths were used for learning, and the features learned by the two networks were adopted for classification. The experimental results show that, the ILBPSDNet proposed had certain recognition abilities in many character data sets, and the network parameters and computation were also reduced. Therefore, it has a significant effect in realizing the application of real-time character recognition. Finally, compared with other latest networks, its network performance could be maintained at a certain level.

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English Word Recognition System Based on BP Neural Network

2024

Lecture Notes on Data Engineering and Communications Technologies

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A Hybrid Character Recognition Approach Using Fuzzy Logic and Stroke Bayesian Program Learning With Naïve Bayes in Industrial Environments

Cited by 5 publications

References 65 publications

A Collaborative Region Detection and Grading Framework for Forest Fire Smoke Using Weakly Supervised Fine Segmentation and Lightweight Faster-RCNN

A Collaborative Region Detection and Grading Framework for Forest Fire Smoke Using Weakly Supervised Fine Segmentation and Lightweight Faster-RCNN

ILBPSDNet: Based on improved local binary pattern shallow deep convolutional neural network for character recognition

English Word Recognition System Based on BP Neural Network

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