An Unequal Deep Learning Approach for 3-D Point Cloud Segmentation

Wang, Junliang; Xu, Chuqiao; Lu, Dai; Zhang, Jie; Zhong, Ray Y.

doi:10.1109/tii.2020.3044106

Cited by 24 publications

(7 citation statements)

References 36 publications

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“…In recent years, in the feld of computer vision, target detection and tracking technology have made great progress, and a series of target detection algorithms and target tracking algorithms with superior performance have emerged, which make their application possible [15][16][17]. Te application of target detection and tracking method to power workplace to realize production intelligence has become a key research topic, which has an important application value in the feld of power production safety [18][19][20].…”

Section: Introductionmentioning

confidence: 99%

“…Te traditional method based on the feature extraction of power equipment has poor adaptability due to the complexity of operation and migration. In the face of complex situations such as rapid illumination change, similar environmental background color to the target and slow movement of the target, the traditional target detection methods usually perform poorly, are greatly afected by the interference of environmental noise, and have the problem of too high time complexity [19,20]. Terefore, limited by the complexity of the actual environment of power production and workplace, the traditional target detection methods cannot meet the needs of power production and operation.…”

Section: Introductionmentioning

confidence: 99%

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Power Operation Violation Identification Method Based on Point Cloud Data Preprocessing and Deep Learning under the Architecture of IoT

Yang

Zhang

et al. 2023

Journal of Electrical and Computer Engineering

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Aiming at the problems of low recognition accuracy and large memory occupation when using point cloud information for power operation violation, A power operation violation recognition method based on point cloud data preprocessing and deep learning under the architecture of Internet of things (IoT) is proposed. First, voxel filtering and statistical filtering methods are used to properly simplify the power operation point cloud data on the premise of ensuring the quality of reverse modeling, and the moving least square method is used to smooth the point cloud to obtain a complete and closed three-dimensional model; second, the process of power operation violation behavior recognition is divided into two stages. In the first stage, PointRCNN extracts the semantic features of each point, separates the front scenic spots, and extracts the preselection box. In the second stage, the candidate box is refined by integrating the semantic features and classification confidence of the first stage to obtain a more accurate bounding box. Finally, the experiments show that the average accuracy of the proposed method is the highest, with an average accuracy of 0.919 in the simple difficulty scenario, 0.897 in the medium difficulty scenario, and 0.839 in the difficult difficulty scenario, which are higher than those of the compared methods. Therefore, the proposed method can effectively improve the accuracy of power operation violation identification.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Power Operation Violation Identification Method Based on Point Cloud Data Preprocessing and Deep Learning under the Architecture of IoT

Yang

Zhang

et al. 2023

Journal of Electrical and Computer Engineering

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“…Furthermore, the possibility of finding different categories of defects is also various, which leads to the imbalance between different categories of defects. In addition, the feature patterns of different categories are different, which leads to the imbalance of the learning ability of the same deep learning model (DLM) for different categories (Wang et al 2020a). C) Strong interference.…”

Section: Challenges For Dlbiiprmentioning

confidence: 99%

Deep learning for industrial image: challenges, methods for enriching the sample space and restricting the hypothesis space, and possible issue

Liu

Bao

Wang

et al. 2021

International Journal of Computer Integrated Manufacturing

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Deep learning (DL) is an important enabling technology for intelligent manufacturing. The DLbased industrial image pattern recognition (DLBIIPR) plays a vital role in the improvement of product quality and production efficiency. Although DL technology has been widely used in the field of natural image, industrial image often has some mixed characteristics, such as small sample, imbalance, small target, strong interference, fine-grained, temporality and semantical, which reduce the feasibility and generalization of DLBIIPR. To solve this problem, this paper provides an overview of approaches commonly used in industry by enriching the sample space and limiting the hypothesis space. In order to improve the confidence of front-line workers in using DL models, the explainable deep learning (XDL) methods are reviewed, and a case study is used to verify the effectiveness of XDL.

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“…Furthermore, as the different extracted characteristics have different layers, the transfer learning strategy based on the hierarchical finetuning was designed in this present paper. The major network was divided into two parts, namely the common characteristic extraction layer and specific characteristic extraction layer, in accordance with the extraction characteristic of each layer [32]. The common characteristics reflect the similar characteristics of the SWFS at different WIP levels, while the specific characteristics reflect the unique characteristics of the SWFS at different WIP levels.…”

Section: Architecture Of Hierarchical Finetuning Transfer Networkmentioning

confidence: 99%

Hierarchical Transfer Learning for Cycle Time Forecasting for Semiconductor Wafer Lot under Different Work in Process Levels

Wang

Gao

et al. 2021

Mathematics

Self Cite

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The accurate cycle time (CT) prediction of the wafer fabrication remains a tough task, as the system level of work in process (WIP) is fluctuant. Aiming to construct one unified CT forecasting model under dynamic WIP levels, this paper proposes a transfer learning method for finetuning the predicted neural network hierarchically. First, a two-dimensional (2D) convolutional neural network was constructed to predict the CT under a primary WIP level with the input of spatial-temporal characteristics by reorganizing the input parameters. Then, to predict the CT under another WIP level, a hierarchical optimization transfer learning strategy was designed to finetune the prediction model so as to improve the accuracy of the CT forecasting. The experimental results demonstrated that the hierarchically transfer learning approach outperforms the compared methods in the CT forecasting with the fluctuation of WIP levels.

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An Unequal Deep Learning Approach for 3-D Point Cloud Segmentation

Cited by 24 publications

References 36 publications

Power Operation Violation Identification Method Based on Point Cloud Data Preprocessing and Deep Learning under the Architecture of IoT

Power Operation Violation Identification Method Based on Point Cloud Data Preprocessing and Deep Learning under the Architecture of IoT

Deep learning for industrial image: challenges, methods for enriching the sample space and restricting the hypothesis space, and possible issue

Hierarchical Transfer Learning for Cycle Time Forecasting for Semiconductor Wafer Lot under Different Work in Process Levels

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