{"title":"基于波斯语文本的卷积神经网络交通标志检测:一个新的数据集","authors":"Saba Kheirinejad, Noushin Riaihi, R. Azmi","doi":"10.1109/ICCKE50421.2020.9303646","DOIUrl":null,"url":null,"abstract":"Recently, traffic panel detection has attracted both academic and industrial attention. However, there are a few works that studied text based traffic panels. This is because there are many challenges in this kind of traffic panels. To obtain an appropriate accuracy in text recognition in the text based traffic panels, we need to detect the panel. Since there is no public text based traffic panels dataset, we collected a new dataset included the Persian text based traffic panels in the streets of Tehran-Iran for the first time. Our dataset contains two sets of figures. The first set has 9294 pictures and the second set has 3305 pictures. The second dataset is more uniform than the first dataset. Therefore, we exploit the first set as an additional dataset and use the second one as the main dataset. Accordingly, we pretrain the network by the additional dataset and train it by the main dataset. We use the tiny YOLOv3 (You Only Look Once version three) algorithm to pretrain, train, and test the dataset. The algorithm is fast and has low complexity. We use K-fold cross validation method to appraise efficiency of the algorithm. From the results section we could see that Precision is 0.973, Recall is 0.945, and Fmeasure is 0.955.","PeriodicalId":402043,"journal":{"name":"2020 10th International Conference on Computer and Knowledge Engineering (ICCKE)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2020-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"4","resultStr":"{\"title\":\"Persian Text Based Traffic sign Detection with Convolutional Neural Network: A New Dataset\",\"authors\":\"Saba Kheirinejad, Noushin Riaihi, R. Azmi\",\"doi\":\"10.1109/ICCKE50421.2020.9303646\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Recently, traffic panel detection has attracted both academic and industrial attention. However, there are a few works that studied text based traffic panels. This is because there are many challenges in this kind of traffic panels. To obtain an appropriate accuracy in text recognition in the text based traffic panels, we need to detect the panel. Since there is no public text based traffic panels dataset, we collected a new dataset included the Persian text based traffic panels in the streets of Tehran-Iran for the first time. Our dataset contains two sets of figures. The first set has 9294 pictures and the second set has 3305 pictures. The second dataset is more uniform than the first dataset. Therefore, we exploit the first set as an additional dataset and use the second one as the main dataset. Accordingly, we pretrain the network by the additional dataset and train it by the main dataset. We use the tiny YOLOv3 (You Only Look Once version three) algorithm to pretrain, train, and test the dataset. The algorithm is fast and has low complexity. We use K-fold cross validation method to appraise efficiency of the algorithm. From the results section we could see that Precision is 0.973, Recall is 0.945, and Fmeasure is 0.955.\",\"PeriodicalId\":402043,\"journal\":{\"name\":\"2020 10th International Conference on Computer and Knowledge Engineering (ICCKE)\",\"volume\":\"10 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2020-10-29\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"4\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2020 10th International Conference on Computer and Knowledge Engineering (ICCKE)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICCKE50421.2020.9303646\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2020 10th International Conference on Computer and Knowledge Engineering (ICCKE)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICCKE50421.2020.9303646","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 4
摘要
近年来,交通面板检测受到了学术界和工业界的广泛关注。然而,有一些研究基于文本的流量面板的作品。这是因为这种交通面板有很多挑战。为了在基于文本的交通面板中获得适当的文本识别精度,需要对面板进行检测。由于没有基于公共文本的交通面板数据集,我们首次收集了一个新的数据集,其中包括伊朗德黑兰街道上基于波斯语文本的交通面板。我们的数据集包含两组数字。第一组有9294张,第二组有3305张。第二个数据集比第一个数据集更加统一。因此,我们利用第一组作为附加数据集,并使用第二组作为主数据集。相应地,我们使用附加数据集对网络进行预训练,并使用主数据集对网络进行训练。我们使用微小的YOLOv3 (You Only Look Once version 3)算法来预训练、训练和测试数据集。该算法速度快,复杂度低。我们使用K-fold交叉验证方法来评估算法的效率。从结果部分我们可以看到Precision是0.973,Recall是0.945,Fmeasure是0.955。
Persian Text Based Traffic sign Detection with Convolutional Neural Network: A New Dataset
Recently, traffic panel detection has attracted both academic and industrial attention. However, there are a few works that studied text based traffic panels. This is because there are many challenges in this kind of traffic panels. To obtain an appropriate accuracy in text recognition in the text based traffic panels, we need to detect the panel. Since there is no public text based traffic panels dataset, we collected a new dataset included the Persian text based traffic panels in the streets of Tehran-Iran for the first time. Our dataset contains two sets of figures. The first set has 9294 pictures and the second set has 3305 pictures. The second dataset is more uniform than the first dataset. Therefore, we exploit the first set as an additional dataset and use the second one as the main dataset. Accordingly, we pretrain the network by the additional dataset and train it by the main dataset. We use the tiny YOLOv3 (You Only Look Once version three) algorithm to pretrain, train, and test the dataset. The algorithm is fast and has low complexity. We use K-fold cross validation method to appraise efficiency of the algorithm. From the results section we could see that Precision is 0.973, Recall is 0.945, and Fmeasure is 0.955.