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WaferSegClassNet - A light-weight network for classification and segmentation of semiconductor wafer defects

COMPUTERS IN INDUSTRY 2022年 142卷

作者： Nag, Subhrajit Makwana, Dhruv Teja, Sai Chandra R. Mittal, Sparsh Mohan, C. Krishna IIT Hyderabad Hyderabad India CKMVigil Pvt Ltd Hyderabad India IIT Roorkee Roorkee Uttar Pradesh India

As the integration density and design intricacy of semiconductor wafers increase, the magnitude and complexity of defects in them are also on the rise. Since the manual inspection of wafer defects is costly, an automated artificial intelligence (AI) based computer-vision approach is highly desired. The previous works on defect analysis have several limitations, such as low accuracy and the need for separate models for classification and segmentation. For analyzing mixed-type defects, some previous works require separately training one model for each defect type, which is non-scalable. In this paper, we present WaferSegClassNet (WSCN), a novel network based on encoder-decoder architecture. WSCN performs simultaneous classification and segmentation of both single and mixed-type wafer defects. WSCN uses a "shared encoder" for classification, and segmentation, which allows training WSCN end-to-end. We use N-pair contrastive loss to first pretrain the encoder and then use BCE-Dice loss for segmentation, and categorical cross-entropy loss for classification. Use of N-pair contrastive loss helps in better embedding representation in the latent dimension of wafer maps. WSCN has a model size of only 0.51MB and performs only 0.2 M FLOPS. Thus, it is much lighter than other state-of-the-art models. Also, it requires only 150 epochs for convergence, compared to 4000 epochs needed by a previous work. We evaluate our model on the MixedWM38 dataset, which has 38,015 images. WSCN achieves an average classification accuracy of 98.2% and a dice coefficient of 0.9999. We are the first to show segmentation results on the MixedWM38 dataset. The source code can be obtained from https://***/ckmvigil/WaferSegClassNet. (C) 2022 Elsevier B.V. All rights reserved.

关键词： Image classification Image Segmentation Semiconductor wafer defect analysis Convolution neural network encoder-decoder architecture

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Inspection route prediction in substation using long short-term memory networks

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COMPUTERS & ELECTRICAL ENGINEERING 2022年 98卷 107680-107680页

作者： Yang, Yingyi Yang, Fan Wu, Hao China Southern Power Grid Technol Co Ltd Robot Div Guangzhou 510170 Guangdong Peoples R China Guangdong Engn Res Ctr Special Robots Special Ind Guangzhou 510170 Guangdong Peoples R China Guangdong Power Grid Co Shaoguan Power Supply Bur Shaoguan 512000 Guandong Peoples R China

Inspection route prediction in real-time is still a huge challenge for the following aspects. Firstly, the equipment in substations to be inspected not only related to spatial distribution, but also geographical environment, equipment types, and real-time status, etc. Secondly, the inspection route may be affected stochastically by emergencies. To tackle these problems, the long and short-term memory recurrent neural network with an attention mechanism has been proposed to predict the inspection route automatically. The model can predict the route in real-time based on the current position coordinate. To verify the validity of this model, the architecture with simulation experiments has been implemented. The hit rate of the predicted route with simulated data reaches 86% on the sets of 1000 training data. Experimental results on the route prediction task demonstrate convincingly that our model is effective and valuable.

关键词： Inspection route prediction Long short-term memory Attention mechanism encoder-decoder architecture Square loss function

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A tutorial review of neural network modeling approaches for model predictive control

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COMPUTERS & CHEMICAL ENGINEERING 2022年 165卷

作者： Ren, Yi Ming Alhajeri, Mohammed S. Luo, Junwei Chen, Scarlett Abdullah, Fahim Wu, Zhe Christofides, Panagiotis D. Univ Calif Los Angeles Dept Chem & Biomol Engn Los Angeles CA 90095 USA Univ Calif Los Angeles Dept Elect & Comp Engn Los Angeles CA 90095 USA Kuwait Univ Dept Chem Engn Safat 13060 Kuwait Natl Univ Singapore Dept Chem & Biomol Engn Singapore 117585 Singapore

An overview of the recent developments of time-series neural network modeling is presented along with its use in model predictive control (MPC). A tutorial on the construction of a neural network-based model is provided and key practical implementation issues are discussed. A nonlinear process example is introduced to demonstrate the application of different neural network-based modeling approaches and evaluate their performance in terms of closed-loop stability and prediction accuracy. Finally, the paper concludes with a brief discussion of future research directions on neural network modeling and its integration with MPC.

关键词： Time-series forecasting Feed-forward neural networks Recurrent neural networks encoder-decoder architecture Model predictive control Nonlinear systems Chemical processes

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RoboPV: An integrated software package for autonomous aerial monitoring of large scale PV plants

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ENERGY CONVERSION AND MANAGEMENT 2022年 254卷 115217-115217页

作者： Sizkouh, A. M. Moradi Esmailifar, S. M. Aghaei, M. Karimkhani, M. Concordia Univ Dept Elect & Comp Engn Montreal PQ H3G 1M8 Canada Amirkabir Univ Technol Sch Mech Aerosp & Marine Engn Tehran 158754413 Iran Norwegian Univ Sci & Technol NTNU Dept Ocean Operat & Civil Engn N-6009 Alesund Norway Univ Freiburg Dept Sustainable Syst Engn INATECH D-79110 Freiburg Germany

In this paper, a novel software package, called RoboPV, is introduced for autonomous aerial monitoring of PV plants. RoboPV automatically performs aerial monitoring of PV plants, from optimal trajectory planning to image processing and pattern recognition for real-time fault detection and analysis. RoboPV consists of four integrated components: boundary area detection, path planning, dynamic processing, and fault detection. To design an optimal flight path, aerial images of PV plants, which have been collected from experimental flights, are given as inputs to a developed encoder-decoder deep learning architecture to extract boundary points of PV plants automatically. Then, a novel path planning algorithm is conducted by RoboPV to design an optimal flight path with full coverage of whole regions of the PV plant. Aerial images are analysed in real-time during the flight by a high precise neural network trained for automatic fault detection. In this study, several decision-making and maneuver algorithms were developed for various real-world flight conditions to improve the performance of RoboPV during an autonomous aerial inspection. RoboPV is a modular processing library that can be installed on any micro-computer processor with a low computational power. Moreover, supporting the MAVLink communication protocol enables RoboPV to connect with an intelligent Pixhawk flight autopilot and navigate a wide range of multi-rotors. To demonstrate the performance of RoboPV, a six degrees of freedom dynamic model of a multi-rotor is developed in a SIMULINK environment with a defined aerial monitoring mission on three different real megawatt-scale PV plants. The results prove that RoboPV can execute the autonomous aerial inspection with an overall accuracy of 93% for large-scale PV plants.

关键词： Photovoltaic (PV) plants Autonomous aerial monitoring Multi-rotor encoder-decoder architecture Automatic fault detection Path planning

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A robust edge-guided and color-guided network for single image dehazing

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COMPUTERS & ELECTRICAL ENGINEERING 2022年 103卷

作者： Xu, Yujie Zhang, Yongjun Cui, Jingrong Cui, Zhongwei Yang, Yitong Guizhou Univ Inst Artificial Intelligence Coll Comp Sci & Technol State Key Lab Publ Big Data Guiyang 550025 Guizhou Peoples R China South China Agr Univ Sch Math & Informat Guangzhou 510642 Guangdong Peoples R China GuiZhou Educ Univ Sch Math & Big Datam Guiyang 550001 Guizhou Peoples R China

Most current dehazing methods tend to cause color distortion and blurred edge details. In this work, we design a robust edge-guided and color-guided dehazing network (ECGDN) to mitigate these issues. ECGDN consists of a cross-aggregation dual U-Net (CADU), a color feature extractor (CFE), a prior attention module(PAM), and a prior fusion group (PFG). Specifically, the CADU can take advantage of the complementarity between edge and texture features in a cross-aggregation manner. The CFE focuses on Hue-and Saturation-channel features to solve color distortion under the constraint of color correction loss. Finally, the PAM and the PFG can handle different priors(i.e., edge, texture, color) with attention mechanism and fusion strategy, respectively, to guide the feature learning procedures. To demonstrate the effectiveness of the proposed method, we perform extensive experiments on synthetic and real-world hazy images. The experimental results show that our method outperforms existing state-of-the-art methods in terms of accuracy and robustness.

关键词： Image dehazing Convolutional neural network Attention mechanism encoder-decoder architecture

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Multi-scale feature fusion network for pixel-level pavement distress detection

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AUTOMATION IN CONSTRUCTION 2022年第0期141卷

作者： Zhong, Jingtao Zhu, Junqing Huyan, Ju Ma, Tao Zhang, Weiguang Southeast Univ Sch Transportat Nanjing 211189 Peoples R China

Automatic pavement distress detection is essential to monitoring and maintaining pavement condition. Currently, many deep learning-based methods have been utilized in pavement distress detection. However, distress segmentation remains as a challenge under complex pavement conditions. In this paper, a novel deep neural network architecture, W-segnet, based on multi-scale feature fusions, is proposed for pixel-wise distress segmentation. The proposed W-segnet concatenates distress location information with distress classification features in two symmetric encoder-decoder structures. Three major types of distresses: crack, pothole, and patch are segmented and the results were discussed. Experimental results show that the proposed W-segnet is robust in various scenarios, achieving a mean pixel accuracy (MPA) of 87.52% and a mean intersection over union (MIoU) of 75.88%. The results demonstrate that W-segnet outperforms other state-of-the-art semantic segmentation models of U-net, SegNet, and PSPNet. Comparison of cost of model training and inference indicates that W-segnet has the largest number of parameters, which needs a slightly longer training time while it does not increase the inference cost. Four public datasets were used to test the generalization ability of the proposed model and the results demonstrate that the W-segnet possesses well segmentation performance.

关键词： Deep learning encoder-decoder architecture Pavement distress Feature fusion Semantic segmentation Unmanned aerial vehicle (UAV)

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Unidirectional RGB-T salient object detection with intertwined driving of encoding and fusion

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ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2022年第0期114卷

作者： Wang, Jie Song, Kechen Bao, Yanqi Yan, Yunhui Han, Yahong Northeastern Univ Sch Mech Engn & Automat Shenyang Liaoning Peoples R China Tianjin Univ Coll Intelligence & Comp Tianjin Peoples R China

The U-shaped encoder-decoder architecture based on CNNs has been rooted in salient object detection (SOD) tasks, and it have revealed two drawbacks while driving the rapid development of saliency detection. (1) The inherent characteristics of CNNs dictate that it is difficult to learn long-range dependencies and model global correlations. (2) For the common purpose of improving the performance of saliency detection, the encoder and decoder should complement each other and work together. However, the existing encoder-decoder architecture treats encoder and decoder independently of each other. Specifically, the encoder is responsible for extracting features and the decoder fuses multi-level or multi-modal features to produce prediction maps. That is, the encoder alone needs to be responsible for the decoder, while the valuable information after the decoder fusion will not facilitate feature extraction. Therefore, we propose a unidirectional RGB-T salient object detection network with intertwined driving of encoding and fusion to solve the above problems. Firstly, we introduce transformer (SegFormer) as the backbone of the network to deal with the problem that CNNs are difficult to establish long-range dependence. Secondly, we constructed a unidirectional architecture where encoding and fusion are intertwined and mutually driving, which discards the drawbacks of encoder-decoder architecture to make the network more powerful and concise. Based on the unidirectional architecture, the proposed Local Detail-driven Fusion Module (LDFM) uses the fused features of the previous level to drive the cross-modal fusion at the current level. Meanwhile, the proposed Local Detail-driven Weighting Module (LDWM) uses the fused features to drive the cross-modal weighting. They will drive more effective features to be fed into the next level of the encoding block. Comprehensive experiments have verified the superior performance of our method on the RGB-T saliency detection task.

关键词： Salient object detection RGB-T Transformer encoder-decoder architecture

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An Adaptive Multiscale Fusion Network Based on Regional Attention for Remote Sensing Images

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IEEE ACCESS 2020年 8卷 107802-107813页

作者： Lu, Wanzhen Liang, Longxue Wu, Xiaosuo Wang, Xiaoyu Cai, Jiali Lanzhou Jiaotong Univ Elect & Informat Engn Dept Lanzhou 730073 Peoples R China Gansu Acad Sci Inst Sensor Technol Lanzhou 730070 Peoples R China Lanzhou Jiaotong Univ Key Lab Opt Technol & Intelligent Control Minist Educ Lanzhou 730070 Peoples R China

With the widespread application of semantic segmentation in remote sensing images with high-resolution, how to improve the accuracy of segmentation becomes a research goal in the remote sensing field. An innovative Fully Convolutional Network (FCN) is proposed based on regional attention for improving the performance of the semantic segmentation framework for remote sensing images. The proposed network follows the encoder-decoder architecture of semantic segmentation and includes the following three strategies to improve segmentation accuracy. The enhanced GCN module is applied to capture the semantic features of remote sensing images. MGFM is proposed to capture different contexts by sampling at different densities. Furthermore, RAM is offered to assign large weights to high-value information in different regions of the feature map. Our method is assessed on two datasets: ISPRS Potsdam dataset and CCF dataset. The results indicate that our model with those strategies outperforms baseline models (DCED50) concerning F1, mean IoU and PA, 10.81%,19.11%, and 11.36% on the Potsdam dataset and 29.26%, 27.64% and 13.57% on the CCF dataset.

关键词： Convolution Feature extraction Remote sensing Image segmentation Semantics Random access memory Kernel Remote sensing fully convolutional networks semantic segmentation encoder-decoder architecture regional attention Potsdam dataset CCF dataset

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Pavement crack detection network based on pyramid structure and attention mechanism

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IET IMAGE PROCESSING 2020年第8期14卷 1580-1586页

作者： Xiang, Xuezhi Zhang, Yuqi El Saddik, Abdulmotaleb Harbin Engn Univ Sch Informat & Commun Engn Harbin 150001 Peoples R China Univ Ottawa Sch Elect Engn & Comp Sci Ottawa ON K1N 6N5 Canada

Automatic detection of pavement crack is an important task for conducting road maintenance. However, as an important part of the intelligent transportation system, automatic pavement crack detection is challenging due to the poor continuity of cracks, the different width of cracks, and the low contrast between cracks and the surrounding pavement. This study proposes a novel pavement crack detection method based on an end-to-end trainable deep convolution neural network. The authors build the network using the encoder-decoder architecture and adopt a pyramid module to exploit global context information for the complex topology structures of cracks. Moreover, they introduce a spatial-channel combinational attention module into the encoder-decoder network for refining crack features. Further, the dilated convolution is used to reduce the loss of crack details due to the pooling operation in the encoder network. In addition, they introduce a lovasz hinge loss function, which is suitable for small objects. They train the authors' network on the CRACK500 dataset and evaluate it on three pavement crack datasets. Among the methods they compare, their method can achieve the best experimental results.

关键词： learning (artificial intelligence) object detection crack detection roads structural engineering computing image recognition cracks maintenance engineering convolutional neural nets intelligent transportation systems pavement crack detection network pyramid structure attention mechanism automatic pavement crack detection surrounding pavement spatial-channel combinational attention module crack features CRACK500 dataset pavement crack datasets encoder-decoder network road maintenance encoder-decoder architecture end-to-end trainable deep convolution neural network complex topology structures pooling operation intelligent transportation system

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Seismic Facies Analysis Based on Deep Learning

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IEEE GEOSCIENCE AND REMOTE SENSING LETTERS 2020年第7期17卷 1119-1123页

作者： Zhang, Yuxi Liu, Yang Zhang, Haoran Xue, Hao China Univ Petr State Key Lab Petr Resources & Prospecting Beijing 102249 Peoples R China China Univ Petr Sch Petr Karamay 834000 Peoples R China CNOOC Int Ltd Beijing 100028 Peoples R China

Seismic facies analysis is to study the sedimentary environment of stratigraphic sequence and provides an important basis for reservoir prediction. Most of the existing analysis methods have low efficiency and heavily rely on manual experience, and therefore, it is difficult to interpret increasingly complex seismic data. Deep learning techniques can help to solve these problems and achieve automatic seismic facies classification. We regard seismic facies classification as a target segmentation problem and propose new method and training strategies. Our workflow primarily involves four sections. First, we process the manually annotated labels and seismic data with mirroring and cropping operations to ensure that network can accept input with arbitrary size and the model training is not limited to GPU memory. Second, data augmentation is applied to automatically generate massive training samples from the processed data. Third, we build two independent networks based on encoder-decoder architecture: one identifies all seismic facies simultaneously, and the other identifies single seismic facies in each model. However, both the results of the two networks have some drawbacks. Fourth, to overcome these drawbacks, we propose an ensemble learning method to get optimized model and test it on 3-D seismic data. The testing results manifest that the proposed method can improve the predictive ability of model, accurately describe the seismic facies, and can be applicable to entire seismic data volume.

关键词： Training Testing Data models Deep learning Reservoirs Graphics processing units Decoding Deep learning encoder-decoder architecture ensemble learning seismic facies analysis

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