Tianmin Yan, Haitao Deng, Yuanpeng Lin, Xueli Yang
The current conventional train hook crack repair technology is mainly used to remanufacture and repair worn hooks by laser cladding repair technology, which leads to poor crack identification due to the lack of simulation and analysis of crack data. In this regard, a multimodal information fusion-based crack repair method for train hooks is proposed. The attention mechanism based on the attributes of multimodal information fusion is used to fuse the multi-scale image alignment method and calculate the crack image region features to realize the recognition of hook cracks. Based on this, numerical simulations of train hook crack repair are performed, and the repair process is optimized. In the experiments, the proposed method is verified for the crack recognition effect. The experimental results show that the proposed method has a high recognition accuracy and ideal crack recognition effect when the proposed method is used to recognize train hook images.
{"title":"A multi-modal information fusion-based method for repairing cracks in train hooks","authors":"Tianmin Yan, Haitao Deng, Yuanpeng Lin, Xueli Yang","doi":"10.1117/12.3000835","DOIUrl":"https://doi.org/10.1117/12.3000835","url":null,"abstract":"The current conventional train hook crack repair technology is mainly used to remanufacture and repair worn hooks by laser cladding repair technology, which leads to poor crack identification due to the lack of simulation and analysis of crack data. In this regard, a multimodal information fusion-based crack repair method for train hooks is proposed. The attention mechanism based on the attributes of multimodal information fusion is used to fuse the multi-scale image alignment method and calculate the crack image region features to realize the recognition of hook cracks. Based on this, numerical simulations of train hook crack repair are performed, and the repair process is optimized. In the experiments, the proposed method is verified for the crack recognition effect. The experimental results show that the proposed method has a high recognition accuracy and ideal crack recognition effect when the proposed method is used to recognize train hook images.","PeriodicalId":210802,"journal":{"name":"International Conference on Image Processing and Intelligent Control","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-08-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132316461","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}
Convolutional neural network-based target detection algorithms are widely used in vehicle detection due to their high speed and accuracy. However, existing algorithms are characterized by large computational volumes, complex network structures, and severe resource constraints. They make them difficult to be ported to mobile platforms and embedded devices. Therefore, the structure of the relevant target detection algorithm needs to be optimized to enable wider deployment of the algorithm. To address the problems mentioned earlier, a YOLOv5SCB lightweight target detection network model is proposed. In the presented model, Shufflenetv2 and CA module are introduced into the backbone network to reduce the complexity of the network model and improve the detection accuracy of the model. Furthermore, BiFPN is integrated into the neck network to improve the efficiency of network feature fusion and enhance the ability of network feature expression. The experimental data show that compared with the original YOLOv5, the model parameters of the proposed YOLOv5SCB are reduced by 62.4% and the overall detection accuracy is improved by 1.1%.
{"title":"The design of lightweight vehicle detection model based on improved YOLOv5","authors":"Wenyu Jiang, Jiayan Wen, G. Xie, Kene Li","doi":"10.1117/12.3001012","DOIUrl":"https://doi.org/10.1117/12.3001012","url":null,"abstract":"Convolutional neural network-based target detection algorithms are widely used in vehicle detection due to their high speed and accuracy. However, existing algorithms are characterized by large computational volumes, complex network structures, and severe resource constraints. They make them difficult to be ported to mobile platforms and embedded devices. Therefore, the structure of the relevant target detection algorithm needs to be optimized to enable wider deployment of the algorithm. To address the problems mentioned earlier, a YOLOv5SCB lightweight target detection network model is proposed. In the presented model, Shufflenetv2 and CA module are introduced into the backbone network to reduce the complexity of the network model and improve the detection accuracy of the model. Furthermore, BiFPN is integrated into the neck network to improve the efficiency of network feature fusion and enhance the ability of network feature expression. The experimental data show that compared with the original YOLOv5, the model parameters of the proposed YOLOv5SCB are reduced by 62.4% and the overall detection accuracy is improved by 1.1%.","PeriodicalId":210802,"journal":{"name":"International Conference on Image Processing and Intelligent Control","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-08-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115102766","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}
Image-based automatic identification of the cotton and flax fibers is extremely significant for the content quantitatively assaying in the textile industry. In this paper, a fiber identification method based on multi-scale features of the texture and Gaussian Process Classification (GPC) is proposed. Firstly, the images of the fibers are collected by an optical microscope and a set of image preprocessing approaches including image enhancement, local binarization, morphological processing is utilized to extract the fibers from the background. Next, the single fiber images are analyzed by the Discrete Wavelet Transform (DWT) and obtain the multiple-scale features of the texture. Then, the Gray Level Co-occurrence Matrix (GLCM) is applied to describe the spatial distribution features. Subsequently, extract the statistical feature from the GLCM and obtain a 42- dimensional feature vector that contains the fiber texture. Finally, 2610 images are randomly divided into train set and test set, and the recognition expert system based on the GPC is trained and validated accordingly. The test results on the test set showed that the classification precision - recall for cotton and flax fibers reached 96% - 97% and 97% - 95%, respectively. The method proposed in this paper can help workers quickly identify cotton fibers and flax fibers for further work, such as calculating the blending ratio of blended fabrics.
{"title":"Research on cotton and flax fiber identification based on multi-scale features of the texture and Gaussian process classification","authors":"Junjie Wei, Hai Bi, Hong Yao, Fangxin Chen","doi":"10.1117/12.3001453","DOIUrl":"https://doi.org/10.1117/12.3001453","url":null,"abstract":"Image-based automatic identification of the cotton and flax fibers is extremely significant for the content quantitatively assaying in the textile industry. In this paper, a fiber identification method based on multi-scale features of the texture and Gaussian Process Classification (GPC) is proposed. Firstly, the images of the fibers are collected by an optical microscope and a set of image preprocessing approaches including image enhancement, local binarization, morphological processing is utilized to extract the fibers from the background. Next, the single fiber images are analyzed by the Discrete Wavelet Transform (DWT) and obtain the multiple-scale features of the texture. Then, the Gray Level Co-occurrence Matrix (GLCM) is applied to describe the spatial distribution features. Subsequently, extract the statistical feature from the GLCM and obtain a 42- dimensional feature vector that contains the fiber texture. Finally, 2610 images are randomly divided into train set and test set, and the recognition expert system based on the GPC is trained and validated accordingly. The test results on the test set showed that the classification precision - recall for cotton and flax fibers reached 96% - 97% and 97% - 95%, respectively. The method proposed in this paper can help workers quickly identify cotton fibers and flax fibers for further work, such as calculating the blending ratio of blended fabrics.","PeriodicalId":210802,"journal":{"name":"International Conference on Image Processing and Intelligent Control","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-08-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134078862","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}
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