{"title":"单发多盒探测器提取铁路设施的基础研究","authors":"Masami Nakamura, Yuta Aoto, Shunji Maeda","doi":"10.1109/ICMLC48188.2019.8949296","DOIUrl":null,"url":null,"abstract":"In railway facilities, there are numerous types and electric train-line facilities. It is difficult to visually inspect all of them, so automatic visual inspection is expected. To achieve automatic inspection, it is important to extract and diagnose the target facilities. This study focuses on facilities extraction by utilizing single-shot multi-box detector (SSD), which can be used as a discriminator for human, car and boat object detection, etc. Diagnosis using Local Subspace Classifier (LSC) is proposed. Herein, we present the evaluation results and the issues applying SSD to the equipment called hangers connecting overhead lines. Some diagnosis results are also explained.","PeriodicalId":221349,"journal":{"name":"2019 International Conference on Machine Learning and Cybernetics (ICMLC)","volume":"18 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2019-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"A Basic Study on Railway Facility Extraction Using a Single-Shot Multi-Box Detector\",\"authors\":\"Masami Nakamura, Yuta Aoto, Shunji Maeda\",\"doi\":\"10.1109/ICMLC48188.2019.8949296\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In railway facilities, there are numerous types and electric train-line facilities. It is difficult to visually inspect all of them, so automatic visual inspection is expected. To achieve automatic inspection, it is important to extract and diagnose the target facilities. This study focuses on facilities extraction by utilizing single-shot multi-box detector (SSD), which can be used as a discriminator for human, car and boat object detection, etc. Diagnosis using Local Subspace Classifier (LSC) is proposed. Herein, we present the evaluation results and the issues applying SSD to the equipment called hangers connecting overhead lines. Some diagnosis results are also explained.\",\"PeriodicalId\":221349,\"journal\":{\"name\":\"2019 International Conference on Machine Learning and Cybernetics (ICMLC)\",\"volume\":\"18 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2019-07-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2019 International Conference on Machine Learning and Cybernetics (ICMLC)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICMLC48188.2019.8949296\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2019 International Conference on Machine Learning and Cybernetics (ICMLC)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICMLC48188.2019.8949296","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
A Basic Study on Railway Facility Extraction Using a Single-Shot Multi-Box Detector
In railway facilities, there are numerous types and electric train-line facilities. It is difficult to visually inspect all of them, so automatic visual inspection is expected. To achieve automatic inspection, it is important to extract and diagnose the target facilities. This study focuses on facilities extraction by utilizing single-shot multi-box detector (SSD), which can be used as a discriminator for human, car and boat object detection, etc. Diagnosis using Local Subspace Classifier (LSC) is proposed. Herein, we present the evaluation results and the issues applying SSD to the equipment called hangers connecting overhead lines. Some diagnosis results are also explained.