{"title":"一种改进的无锚检测交通标志检测方法","authors":"Tonghe Ding, Kaili Feng, Tianping Li, Zhifeng Liu","doi":"10.1109/ISCEIC53685.2021.00079","DOIUrl":null,"url":null,"abstract":"As a basic task in the intelligent driving system, the traffic sign detection can locate and classify the traffic signs in real time and accurately. In response to the own design limitation of anchor-based detection methods, an improved anchor-free detection method is proposed. The method adds feature reinforcement module and head reinforcement module. First, in order to solve the small sign detection problem, a feature reinforcement module based on the hybrid attention mechanism is proposed. Secondly, in order to reduce the interference of complex background information to distinguish the prospects from the background, a head reinforcement module is proposed. We perform sufficient experiments on the CCTSDB dataset, and the experimental results demonstrate the effectiveness and robustness of the proposed method.","PeriodicalId":342968,"journal":{"name":"2021 2nd International Symposium on Computer Engineering and Intelligent Communications (ISCEIC)","volume":"15 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2021-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"An Improved Anchor-Free Detection Method for Traffic Sign Detection\",\"authors\":\"Tonghe Ding, Kaili Feng, Tianping Li, Zhifeng Liu\",\"doi\":\"10.1109/ISCEIC53685.2021.00079\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"As a basic task in the intelligent driving system, the traffic sign detection can locate and classify the traffic signs in real time and accurately. In response to the own design limitation of anchor-based detection methods, an improved anchor-free detection method is proposed. The method adds feature reinforcement module and head reinforcement module. First, in order to solve the small sign detection problem, a feature reinforcement module based on the hybrid attention mechanism is proposed. Secondly, in order to reduce the interference of complex background information to distinguish the prospects from the background, a head reinforcement module is proposed. We perform sufficient experiments on the CCTSDB dataset, and the experimental results demonstrate the effectiveness and robustness of the proposed method.\",\"PeriodicalId\":342968,\"journal\":{\"name\":\"2021 2nd International Symposium on Computer Engineering and Intelligent Communications (ISCEIC)\",\"volume\":\"15 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2021-08-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2021 2nd International Symposium on Computer Engineering and Intelligent Communications (ISCEIC)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ISCEIC53685.2021.00079\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2021 2nd International Symposium on Computer Engineering and Intelligent Communications (ISCEIC)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ISCEIC53685.2021.00079","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
An Improved Anchor-Free Detection Method for Traffic Sign Detection
As a basic task in the intelligent driving system, the traffic sign detection can locate and classify the traffic signs in real time and accurately. In response to the own design limitation of anchor-based detection methods, an improved anchor-free detection method is proposed. The method adds feature reinforcement module and head reinforcement module. First, in order to solve the small sign detection problem, a feature reinforcement module based on the hybrid attention mechanism is proposed. Secondly, in order to reduce the interference of complex background information to distinguish the prospects from the background, a head reinforcement module is proposed. We perform sufficient experiments on the CCTSDB dataset, and the experimental results demonstrate the effectiveness and robustness of the proposed method.