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Liang Xu
School of Automation, Guangdong University of Technology, Guangzhou 510006, China

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Journal article
Published: 10 June 2021 in 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.

ACS Style

Jin Pan; XiaoMing Ou; Liang Xu. A Collaborative Region Detection and Grading Framework for Forest Fire Smoke Using Weakly Supervised Fine Segmentation and Lightweight Faster-RCNN. Forests 2021, 12, 768 .

AMA Style

Jin Pan, XiaoMing Ou, Liang Xu. A Collaborative Region Detection and Grading Framework for Forest Fire Smoke Using Weakly Supervised Fine Segmentation and Lightweight Faster-RCNN. Forests. 2021; 12 (6):768.

Chicago/Turabian Style

Jin Pan; XiaoMing Ou; Liang Xu. 2021. "A Collaborative Region Detection and Grading Framework for Forest Fire Smoke Using Weakly Supervised Fine Segmentation and Lightweight Faster-RCNN." Forests 12, no. 6: 768.

Journal article
Published: 14 July 2020 in IEEE Access
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Accurate localization is critical in the internet of things (IOT), especially for wireless sensor networks (WSNs). Location estimation can be affected by factors such as node density, topological diversity, and sensor coverage. As such, we propose a hybrid approach using multistage collaborative calibration for wireless sensor network localization, specifically in 3D environments. This technique integrates a Modified version of Light Gradient Boosting Model (MLGB), which is based on a regression scheme, a cooperative methodology, and a fine calibration model for collaborative fusion. These techniques were combined with quadrilateral shrunk centroid (QSC) and distance vector hop algorithms, using a multi-communication radius and an improved frog-leaping algorithm (DVMFL). In the first step, MLGB was used to correct for inhomogeneous localization estimation errors and RSSI data sparsity. As a result, the model is capable of adapting to high topological diversity (i.e., C-shape, H-shape, S-shape, and O-shape).Successive steps further improved prediction accuracy by using a screening cooperative anchor node strategy to increase node density and enhance the QSC-DVMFL fusion framework for fine position estimation. The proposed methodology was assessed in a series of validation, comparing it to other techniques. The results demonstrated a clear effectiveness and adaptability across a variety of factors that typically affect WSN localization.

ACS Style

Liang Xu; Zhiliang Li; Xiuxi Li. A Hybrid Approach Using Multistage Collaborative Calibration for Wireless Sensor Network Localization in 3D Environments. IEEE Access 2020, 8, 130205 -130223.

AMA Style

Liang Xu, Zhiliang Li, Xiuxi Li. A Hybrid Approach Using Multistage Collaborative Calibration for Wireless Sensor Network Localization in 3D Environments. IEEE Access. 2020; 8 (99):130205-130223.

Chicago/Turabian Style

Liang Xu; Zhiliang Li; Xiuxi Li. 2020. "A Hybrid Approach Using Multistage Collaborative Calibration for Wireless Sensor Network Localization in 3D Environments." IEEE Access 8, no. 99: 130205-130223.

Journal article
Published: 06 July 2020 in IEEE Access
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Automated character recognition is critical for reading and tracking data in a variety of fields. It is particularly challenging in industrial settings since information may be printed on the surface of various materials with complex and uneven shapes, causing overlapping, obstructing, and distorting characters. We propose a hybrid character recognition approach using fuzzy logic and stroke Bayesian program learning with naïve Bayes. During character segmentation, touching characters are separated using support vector machines and a three-feature fuzzy segmentation strategy that uses particle swarm optimization. This approach includes a new methodology for stroke presentation and extraction using a prebuilt primitive-stroke library containing prior knowledge. During character recognition, a conceptual character model is constructed using stroke Bayesian program learning. Monte Carlo Markov chain sampling is used to produce a fitting model for each character. This model predicts character classification by calculating the probability that a target sample belongs to a training set. To this end, naïve Bayes effectively discerns extremely similar characters. We evaluate our method experimentally using a database of industrial images and the NIST dataset. Our method outperforms existing state-of-the art methods.

ACS Style

Liang Xu; Yuxi Wang; Ruihui Li; Xiaonan Yang; Xiuxi Li. A Hybrid Character Recognition Approach Using Fuzzy Logic and Stroke Bayesian Program Learning With Naïve Bayes in Industrial Environments. IEEE Access 2020, 8, 124767 -124782.

AMA Style

Liang Xu, Yuxi Wang, Ruihui Li, Xiaonan Yang, Xiuxi Li. A Hybrid Character Recognition Approach Using Fuzzy Logic and Stroke Bayesian Program Learning With Naïve Bayes in Industrial Environments. IEEE Access. 2020; 8 ():124767-124782.

Chicago/Turabian Style

Liang Xu; Yuxi Wang; Ruihui Li; Xiaonan Yang; Xiuxi Li. 2020. "A Hybrid Character Recognition Approach Using Fuzzy Logic and Stroke Bayesian Program Learning With Naïve Bayes in Industrial Environments." IEEE Access 8, no. : 124767-124782.

Journal article
Published: 02 March 2020 in IEEE Access
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ACS Style

Liang Xu; Shuai Lv; Yong Deng; Xiuxi Li. A Weakly Supervised Surface Defect Detection Based on Convolutional Neural Network. IEEE Access 2020, 8, 42285 -42296.

AMA Style

Liang Xu, Shuai Lv, Yong Deng, Xiuxi Li. A Weakly Supervised Surface Defect Detection Based on Convolutional Neural Network. IEEE Access. 2020; 8 ():42285-42296.

Chicago/Turabian Style

Liang Xu; Shuai Lv; Yong Deng; Xiuxi Li. 2020. "A Weakly Supervised Surface Defect Detection Based on Convolutional Neural Network." IEEE Access 8, no. : 42285-42296.

Journal article
Published: 24 July 2019 in IEEE Access
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Many deep-learning character recognition methods have been developed over the past few years. Chinese characters are widely used in many countries; however deep learning-based Chinese character recognition methods are faced with various problems, such as a large amount of data required for training, numerous parameters, and a large consumption of computing resources. Concept learning is a hominine learning approach. Unlike existing deep learning models, conceptual model learning can be realized by using as little as one sample. This study is the first to propose a handwritten Chinese character recognition method based on concept learning. Different from the existing image representation-based character recognition methods, the proposed method builds a meta stroke library with prior knowledge, and then presents a Chinese character conceptual model based on stroke relationship learning using a character stroke extraction method and Bayesian program learning. During character recognition, Monte Carlo Markov chain sampling is utilized to obtain the character generation model for each character conceptual. This generation model can calculate the probability of the target and training characters being the same classification, and thereby determines the classification of the target character. The experimental results indicate that, with the proposed method, the conceptual model of each character can be built for character classification prediction using as few as one character sample. Our approach obtains better performance than the state-of-the-art methods on ICDAR-2013 competition dataset.

ACS Style

Liang Xu; Yuxi Wang; Xiuxi Li; Ming Pan. Recognition of Handwritten Chinese Characters Based on Concept Learning. IEEE Access 2019, 7, 102039 -102053.

AMA Style

Liang Xu, Yuxi Wang, Xiuxi Li, Ming Pan. Recognition of Handwritten Chinese Characters Based on Concept Learning. IEEE Access. 2019; 7 (99):102039-102053.

Chicago/Turabian Style

Liang Xu; Yuxi Wang; Xiuxi Li; Ming Pan. 2019. "Recognition of Handwritten Chinese Characters Based on Concept Learning." IEEE Access 7, no. 99: 102039-102053.

Journal article
Published: 26 April 2019 in IEEE Transactions on Instrumentation and Measurement
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Surface defects of explosive cartridge in the automatic sorting process are of small area, irregular shape, and random distribution, all problematic characteristics that hinder surface defect detection. To address these issues, a new multi-defect detection method has been proposed in this study based on a combination of an improved visual attention model and image partitioning-weighted eigenvalue. Firstly, image pre-processing is carried out by a background estimation algorithm. Then, a new fusion operator based on defects discrimination is implemented in a visual attention model to integrate intensity, orientation and edge conspicuity into a saliency graph, which a saliency effect of defects is considered during the fusion process. Thirdly, a saliency map is divided into image blocks based on image variance. This allows for the extraction of image blocks including defects, the calculation of the weighted eigenvalue, and the determination of regions containing multi-defect. The image partitioning-weighted eigenvalue is used to make a decision for multi-defect. The experimental results show this method’s detection accuracy as 98.2%, with a less computation time and quickly detection speed. Therefore, this method could be adopted for on-line detection systems for explosive cartridge surface defects.

ACS Style

Liang Xu; Haibo Xu; Xiuxi Li; Ming Pan. A Defect Inspection for Explosive Cartridge Using an Improved Visual Attention and Image-Weighted Eigenvalue. IEEE Transactions on Instrumentation and Measurement 2019, 69, 1191 -1204.

AMA Style

Liang Xu, Haibo Xu, Xiuxi Li, Ming Pan. A Defect Inspection for Explosive Cartridge Using an Improved Visual Attention and Image-Weighted Eigenvalue. IEEE Transactions on Instrumentation and Measurement. 2019; 69 (4):1191-1204.

Chicago/Turabian Style

Liang Xu; Haibo Xu; Xiuxi Li; Ming Pan. 2019. "A Defect Inspection for Explosive Cartridge Using an Improved Visual Attention and Image-Weighted Eigenvalue." IEEE Transactions on Instrumentation and Measurement 69, no. 4: 1191-1204.

Journal article
Published: 01 June 2016 in Measurement
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This paper is a new study on developing a machine vision system for inspecting the conveying attitudes of columnar objects. The presented system consists of image pre-processing, feature extraction, and attitude diagnosis. First of all, in order to segment the objects from the background (namely image pre-processing), an improved maximum between-class variance method is proposed for searching a histogram peak and calculating a threshold value based on the statistics and probability, to solve the problems caused by the non-uniform brightness in a realistic conveyor belt. Then, an open morphological operation is used to eliminate the noise from the binary images produced in the pre-processing step. In the second step (feature extraction), the features of columnar objects are determined by four methods, edge line detecting method, intercepting method, rectangle locating method and feature statistic method. Finally, the diagnosis for the conveying attitudes of columnar objects is based on a hybrid classifier using random forests, and a fuzzy logic. The proposed system is applied to a realistic process for packing industrial explosives. The results of experiments show that the proposed system allows efficient and accurate 100% inspection for the conveying attitude, which ensures the high speed and steady operation of a packing line.

ACS Style

Liang Xu; Xiaomin He; Xiuxi Li; Ming Pan. A machine-vision inspection system for conveying attitudes of columnar objects in packing processes. Measurement 2016, 87, 255 -273.

AMA Style

Liang Xu, Xiaomin He, Xiuxi Li, Ming Pan. A machine-vision inspection system for conveying attitudes of columnar objects in packing processes. Measurement. 2016; 87 ():255-273.

Chicago/Turabian Style

Liang Xu; Xiaomin He; Xiuxi Li; Ming Pan. 2016. "A machine-vision inspection system for conveying attitudes of columnar objects in packing processes." Measurement 87, no. : 255-273.

Journal article
Published: 20 January 2010 in Journal of Computer Applications
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ACS Style

Liang Xu. Application of nonlinear feature extraction and least square support vector machines for fault diagnosis of chemical process. Journal of Computer Applications 2010, 30, 236 -239.

AMA Style

Liang Xu. Application of nonlinear feature extraction and least square support vector machines for fault diagnosis of chemical process. Journal of Computer Applications. 2010; 30 (1):236-239.

Chicago/Turabian Style

Liang Xu. 2010. "Application of nonlinear feature extraction and least square support vector machines for fault diagnosis of chemical process." Journal of Computer Applications 30, no. 1: 236-239.