{"title":"基于Canny边缘提取的车辆仪表异物检测预警方法研究","authors":"Fei Peng, Yuqiang Shen, Xiaoli Yang","doi":"10.1109/INSAI56792.2022.00046","DOIUrl":null,"url":null,"abstract":"In recent years, in urban traffic, vehicle gauge detection and early warning of abnormal conditions have attracted more and more attention. At present, the method based on edge detection is often used to detect foreign matters in vehicle gauge. However, this method requires manual detection, confirmation and classification, and its accuracy and accuracy are low; and the method of edge detection is only suitable for detecting objects with small vehicle edge, and there is a big problem of detection accuracy for foreign objects in vehicle clearance. Therefore, this paper studies the defects in the vehicle clearance foreign matter detection and early warning problem, and proposes a vehicle fault anomaly detection and early warning method based on Canny edge extraction. Compared with the traditional methods, the detection accuracy and false alarm rate of the detected abnormal vehicle fault information and defects after classification and post-processing are higher. In addition, due to the phenomenon of disconnection and collision in varying degrees, it is also of great significance for the detection of foreign matters in the vehicle limit and the detection and early warning of abnormalities. The experimental results show that this method has high detection speed and accuracy, and can avoid dropping lines, collisions and other phenomena.","PeriodicalId":318264,"journal":{"name":"2022 2nd International Conference on Networking Systems of AI (INSAI)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Research on Detection and Early Warning Method of Foreign Objects in Vehicle Gauge Based on Canny Edge Extraction\",\"authors\":\"Fei Peng, Yuqiang Shen, Xiaoli Yang\",\"doi\":\"10.1109/INSAI56792.2022.00046\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In recent years, in urban traffic, vehicle gauge detection and early warning of abnormal conditions have attracted more and more attention. At present, the method based on edge detection is often used to detect foreign matters in vehicle gauge. However, this method requires manual detection, confirmation and classification, and its accuracy and accuracy are low; and the method of edge detection is only suitable for detecting objects with small vehicle edge, and there is a big problem of detection accuracy for foreign objects in vehicle clearance. Therefore, this paper studies the defects in the vehicle clearance foreign matter detection and early warning problem, and proposes a vehicle fault anomaly detection and early warning method based on Canny edge extraction. Compared with the traditional methods, the detection accuracy and false alarm rate of the detected abnormal vehicle fault information and defects after classification and post-processing are higher. In addition, due to the phenomenon of disconnection and collision in varying degrees, it is also of great significance for the detection of foreign matters in the vehicle limit and the detection and early warning of abnormalities. The experimental results show that this method has high detection speed and accuracy, and can avoid dropping lines, collisions and other phenomena.\",\"PeriodicalId\":318264,\"journal\":{\"name\":\"2022 2nd International Conference on Networking Systems of AI (INSAI)\",\"volume\":\"8 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2022-10-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2022 2nd International Conference on Networking Systems of AI (INSAI)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/INSAI56792.2022.00046\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2022 2nd International Conference on Networking Systems of AI (INSAI)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/INSAI56792.2022.00046","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Research on Detection and Early Warning Method of Foreign Objects in Vehicle Gauge Based on Canny Edge Extraction
In recent years, in urban traffic, vehicle gauge detection and early warning of abnormal conditions have attracted more and more attention. At present, the method based on edge detection is often used to detect foreign matters in vehicle gauge. However, this method requires manual detection, confirmation and classification, and its accuracy and accuracy are low; and the method of edge detection is only suitable for detecting objects with small vehicle edge, and there is a big problem of detection accuracy for foreign objects in vehicle clearance. Therefore, this paper studies the defects in the vehicle clearance foreign matter detection and early warning problem, and proposes a vehicle fault anomaly detection and early warning method based on Canny edge extraction. Compared with the traditional methods, the detection accuracy and false alarm rate of the detected abnormal vehicle fault information and defects after classification and post-processing are higher. In addition, due to the phenomenon of disconnection and collision in varying degrees, it is also of great significance for the detection of foreign matters in the vehicle limit and the detection and early warning of abnormalities. The experimental results show that this method has high detection speed and accuracy, and can avoid dropping lines, collisions and other phenomena.