Proceedings of the 2023 5th International Conference on Pattern Recognition and Intelligent Systems最新文献

Reconstructing 3D Shapes as an Union of Boxes from Multi-View Images 从多视图图像中重建三维形状作为盒子的联合

Proceedings of the 2023 5th International Conference on Pattern Recognition and Intelligent Systems Pub Date : 2023-07-28 DOI: 10.1145/3609703.3609705

Zihan Yang, Minglun Gong

{"title":"Reconstructing 3D Shapes as an Union of Boxes from Multi-View Images","authors":"Zihan Yang, Minglun Gong","doi":"10.1145/3609703.3609705","DOIUrl":"https://doi.org/10.1145/3609703.3609705","url":null,"abstract":"The task of reconstructing object shapes from input images has become increasingly important in various fields, such as computer vision, robotics, augmented reality, video games, and autonomous vehicles. While approaches for reconstructing shapes with varying levels of detail have been proposed, balancing representation accuracy and model complexity remains a challenge. To address this challenge, we propose an end-to-end approach for reconstructing object shapes from multiple images using a union of box primitives. Our approach offers a simpler and more efficient 3D representation of objects without the need for intermediate products such as voxels, resulting in faster inference times. Additionally, we introduce an auxiliary task to aid in learning how to extract and transform spatial features from images without requiring camera calibrations. Extensive experiments demonstrate that our method can produce comparable results to approaches that require 3D voxelized input while utilizing only 2D RGB images as input. Furthermore, our method significantly outperforms the aforementioned approaches in terms of inference time.","PeriodicalId":101485,"journal":{"name":"Proceedings of the 2023 5th International Conference on Pattern Recognition and Intelligent Systems","volume":"96 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115668506","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Identification-Dissemination-Warning: Algorithm and Prediction of Early Warning of Network Public Opinion 识别-传播-预警:网络舆情预警算法与预测

Proceedings of the 2023 5th International Conference on Pattern Recognition and Intelligent Systems Pub Date : 2023-07-28 DOI: 10.1145/3609703.3609723

Lin Sun

引用次数: 0

Multi-population Runge Kutta Optimizer Based on Gaussian Disturbance 基于高斯扰动的多种群Runge Kutta优化器

Proceedings of the 2023 5th International Conference on Pattern Recognition and Intelligent Systems Pub Date : 2023-07-28 DOI: 10.1145/3609703.3609713

Jinhan Chen, Yitong Song, Jixiang Zhu, Sheng-Kai Wang

引用次数: 0

MSYOLOF: Multi-input-single-output encoder network with tripartite feature enhancement for object detection MSYOLOF:多输入-单输出编码器网络与三方特征增强的目标检测

Proceedings of the 2023 5th International Conference on Pattern Recognition and Intelligent Systems Pub Date : 2023-07-28 DOI: 10.1145/3609703.3609710

Gong Cheng, Xi Yong, Xin Lyu, Tao Zeng, Xinyu Wang, Jiale Chen, Xin Li

{"title":"MSYOLOF: Multi-input-single-output encoder network with tripartite feature enhancement for object detection","authors":"Gong Cheng, Xi Yong, Xin Lyu, Tao Zeng, Xinyu Wang, Jiale Chen, Xin Li","doi":"10.1145/3609703.3609710","DOIUrl":"https://doi.org/10.1145/3609703.3609710","url":null,"abstract":"Object detection under one-level feature is a challenging task, which requires that object representations at different scales can be extracted on a single feature map. However, existing object detectors using a one-level feature suffer from inadequate of different-scale object representations resulting in low accuracy for multi-scale object detection, especially for smaller objects. To address the problem above-mentioned, a novel object detector named MSYOLOF, is proposed to construct an effective single feature map for detecting objects of different scales. In the proposed network, three modules are proposed to bring considerable improvements, namely Feature Pyramid Pooling (FPP), Feature Perception Enhancement (FPE), and Dual Branch Receptive Field (DBRF). Firstly, the FPP module aggregates contextual information from various regions to improve the network's ability to achieve global information, which strengthens the model's understanding of the overall scene. Then, the FPE module utilizes coordinate attention to construct a residual block to obtain orientation-aware and position-sensitive information, making the network efficient in accurately locating and identifying objects of interest. Third, by rethinking the Dilated Encoder of YOLOF, the DBRF module reduces information loss and mitigates the problem of being sensitive only to large objects when dilated convolution utilizes large expansion rates. Extensive experiments are conducted on COCO benchmark to validate the effectiveness of our network, which exhibits superior performance compared to other state-of-the-art networks.","PeriodicalId":101485,"journal":{"name":"Proceedings of the 2023 5th International Conference on Pattern Recognition and Intelligent Systems","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117075465","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Fiber Recognition Algorithm Based on Improved Mask RCNN 基于改进掩模RCNN的光纤识别算法

Proceedings of the 2023 5th International Conference on Pattern Recognition and Intelligent Systems Pub Date : 2023-07-28 DOI: 10.1145/3609703.3609719

Zheng-hao Huo, Ziyin Li, Ruide Qu, Xiaodong Wang, Fei Ye, Jun Jin, Xiaojuan Yao

{"title":"Fiber Recognition Algorithm Based on Improved Mask RCNN","authors":"Zheng-hao Huo, Ziyin Li, Ruide Qu, Xiaodong Wang, Fei Ye, Jun Jin, Xiaojuan Yao","doi":"10.1145/3609703.3609719","DOIUrl":"https://doi.org/10.1145/3609703.3609719","url":null,"abstract":"In response to the application requirements of identifying and classifying multiple types of fibers, this paper proposes a fiber recognition algorithm based on improved Mask RCNN to achieve recognition and classification of multiple types of fibers, reduce the labor cost of fiber inspection, and improve inspection efficiency and quality. Firstly, a data augmentation strategy is adopted, which combines three data augmentation methods: RandomFlip, RandomCrop, and Cutout to achieve the best increase in network performance; Subsequently, a multi-scale training strategy is introduced to improve the model's training efficiency while also enhancing its robustness to scale; Finally, the attention mechanism module of convolutional blocks is added to solve the problem of low recognition and classification accuracy caused by small differences in fine-grained granularity between certain fiber classes. The experimental results show that the algorithm achieves a recognition and classification accuracy of 97.87% on the test set by introducing techniques such as data augmentation, multi-scale training, and CBAM, significantly improving the recognition and classification accuracy of various fiber targets.","PeriodicalId":101485,"journal":{"name":"Proceedings of the 2023 5th International Conference on Pattern Recognition and Intelligent Systems","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134142358","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Research on vehicle spare parts demand forecast based on XGBoost-LightGBM 基于XGBoost-LightGBM的汽车备件需求预测研究

Proceedings of the 2023 5th International Conference on Pattern Recognition and Intelligent Systems Pub Date : 2023-07-28 DOI: 10.1145/3609703.3609721

Qianqian Zhu, Liu Yang, Yingnan Liu

{"title":"Research on vehicle spare parts demand forecast based on XGBoost-LightGBM","authors":"Qianqian Zhu, Liu Yang, Yingnan Liu","doi":"10.1145/3609703.3609721","DOIUrl":"https://doi.org/10.1145/3609703.3609721","url":null,"abstract":"Vehicle spare parts demand forecasting is crucial for optimizing inventory and improving maintenance efficiency. This study aims to explore a vehicle spare parts demand forecasting method based on the fusion of XGBoost and LightGBM models to enhance prediction accuracy and precision. In this paper, we first collected a large amount of historical spare parts demand data and associated feature data, followed by data preprocessing and feature engineering. Then, we constructed individual machine learning models as well as the XGBoost-LightGBM fusion model, and performed parameter tuning and optimization using the Optuna framework. Experimental results demonstrate that both XGBoost and LightGBM models achieve favorable performance in spare parts demand forecasting, but the fusion of these two models further enhances prediction accuracy. The fusion model exhibits lower MAPE values compared to individual models on the test set, confirming its superiority and effectiveness. This method leverages the strengths of both models and improves prediction accuracy through weight fusion, offering practical significance in achieving accurate spare parts demand forecasting, optimizing inventory, and improving maintenance efficiency. Future research can explore alternative machine learning algorithms and feature engineering methods to further enhance the accuracy and reliability of vehicle spare parts forecasting.","PeriodicalId":101485,"journal":{"name":"Proceedings of the 2023 5th International Conference on Pattern Recognition and Intelligent Systems","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123324728","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Building Segmentation from Remote Sensing Image via DWT Attention Networks 基于DWT关注网络的遥感影像建筑物分割

Proceedings of the 2023 5th International Conference on Pattern Recognition and Intelligent Systems Pub Date : 2023-07-28 DOI: 10.1145/3609703.3609704

Yin-hua Wu, Mingquan Zhou, Shenglin Geng, Dan Zhang

{"title":"Building Segmentation from Remote Sensing Image via DWT Attention Networks","authors":"Yin-hua Wu, Mingquan Zhou, Shenglin Geng, Dan Zhang","doi":"10.1145/3609703.3609704","DOIUrl":"https://doi.org/10.1145/3609703.3609704","url":null,"abstract":"The attention mechanism has been widely used and achieved good results in many visual tasks. But the calculations of attention mechanism in vision tasks consume huge spaces and times, which is the obvious disadvantage of this method. In order to alleviate this problem, we use the DWT(Discrete Wavelet Transform) method to reduce the complexity of attention calculation. DWT can transform an N-dimensional vector into two vectors, one is the low-frequency component of N/2 dimension and the other is high-frequency component of N/2 dimension too. We only use the low-frequency to calculate the attention matrixes, which can reduce the complexity of matrix multiplication, then the time and space consumption of the network is reduced significantly. We also find that the building segmentation in the remote sensing image is different from the other scene segmentation, that the sizes and numbers of different classes of the targets in the general scene images are obvious. Despite all this, our method is still applicable for the targets with large numbers and sizes in general scene images, but not for the targets with small sizes and numbers, and this view is also verified by the subsequent experiments on different datasets. We apply our method on three typical networks (Danet, Swin and Segmenter), and carry out comprehensive experiments on the Cityscape dataset and three building segmentation datasets (Inria Aerial Dataset, Massachusetts Buildings Dataset and Chinese Style Architecture Dataset). The experiments show that, our method is more suitable for building segmentation and can reduce the complexity of the model calculation in building segmentation, and the Mean IoU of segmentation results is not reduced clearly, some even improved.","PeriodicalId":101485,"journal":{"name":"Proceedings of the 2023 5th International Conference on Pattern Recognition and Intelligent Systems","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129285724","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

CapsNet-based drift detection in data stream mining 数据流挖掘中基于capsnet的漂移检测

Proceedings of the 2023 5th International Conference on Pattern Recognition and Intelligent Systems Pub Date : 2023-07-28 DOI: 10.1145/3609703.3609724

Borong Lin, Nanlin Jin

引用次数: 1

A study on the line loss index of a substation area based on cooperative games with multiple influencing factors 基于多影响因素合作博弈的变电站区域线损指标研究

Proceedings of the 2023 5th International Conference on Pattern Recognition and Intelligent Systems Pub Date : 2023-07-28 DOI: 10.1145/3609703.3609715

Linfeng Wu, Xiaowei Yang, Hao Yang, Zhenhui Zhu, Shunli Chen

{"title":"A study on the line loss index of a substation area based on cooperative games with multiple influencing factors","authors":"Linfeng Wu, Xiaowei Yang, Hao Yang, Zhenhui Zhu, Shunli Chen","doi":"10.1145/3609703.3609715","DOIUrl":"https://doi.org/10.1145/3609703.3609715","url":null,"abstract":"The line loss rate varies significantly among different substation areas due to diverse influencing factors. Consequently, a study is conducted to investigate the line loss index of a substation area by employing a cooperative game approach that considers multiple influencing factors. Firstly, utilizing the available fundamental data of the substation area, construct a substation area factor suitable for the calculation of \"one substation area, one index\". Subsequently, an initial low-voltage substation area line loss prediction model was constructed using Bi-LSTM. Finally, the weights of each influencing factor are calculated using a cooperative game strategy, and the attention mechanism is applied to Bi-LSTM. After the model is trained and optimized, the predicted value for the line loss index for each substation area is output. Experiments indicate that the algorithm can effectively enhances the accuracy of predicting the line loss index value in the substation area, and assist in customized and refined management of loss reduction in the low-voltage distribution substation area.","PeriodicalId":101485,"journal":{"name":"Proceedings of the 2023 5th International Conference on Pattern Recognition and Intelligent Systems","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127563414","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Vehicle Appearance Damage Detection Based on Mask R-CNN 基于掩模R-CNN的车辆外观损伤检测

Proceedings of the 2023 5th International Conference on Pattern Recognition and Intelligent Systems Pub Date : 2023-07-28 DOI: 10.1145/3609703.3609709

Fei Meng, Qianqian Zhu, Xuening Wu

引用次数: 0