{"title":"基于最小计算资源的汽车牌照识别软件","authors":"Kharina Natalya, Chernyadyev Sergei","doi":"10.1109/dspa53304.2022.9790745","DOIUrl":null,"url":null,"abstract":"The paper proposes a software for recognizing car license plates. The software is intended for integration into an autonomous module for installation on a gate at the entrance to the protected area. A feature of the software is the use of computer vision algorithms, the implementation of which requires minimal computing resources, since the video stream is being processed by a low-end CPU. The software is executed in the form of the caused library in language C ++ with use of standard functions of library of computer vision OpenCV. The software consists of following steps: image preprocessing and binarization, license plate localization, plate rotation and normalization, segmentation inside the license plate, segmentation result validation, text recognition. To verify and test the software, a camera was installed on the gate with the subsequent processing of the received video data. As a result of testing probability of correct recognition 0.96, probability of recognition error - 0.004, probability of missing - 0.035, probability of false recognition - 0.015, the frame processing time at a frame resolution of 3 MPix on an Orange Pi Pc 2 CPU with an Allwinner H5 Quad-Core ARM Cortex-A53 64 bit processor is 1.2-1.5 s.","PeriodicalId":428492,"journal":{"name":"2022 24th International Conference on Digital Signal Processing and its Applications (DSPA)","volume":"55 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-03-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Software for Car License Plates Recognition with Minimal Computing Resources\",\"authors\":\"Kharina Natalya, Chernyadyev Sergei\",\"doi\":\"10.1109/dspa53304.2022.9790745\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The paper proposes a software for recognizing car license plates. The software is intended for integration into an autonomous module for installation on a gate at the entrance to the protected area. A feature of the software is the use of computer vision algorithms, the implementation of which requires minimal computing resources, since the video stream is being processed by a low-end CPU. The software is executed in the form of the caused library in language C ++ with use of standard functions of library of computer vision OpenCV. The software consists of following steps: image preprocessing and binarization, license plate localization, plate rotation and normalization, segmentation inside the license plate, segmentation result validation, text recognition. To verify and test the software, a camera was installed on the gate with the subsequent processing of the received video data. As a result of testing probability of correct recognition 0.96, probability of recognition error - 0.004, probability of missing - 0.035, probability of false recognition - 0.015, the frame processing time at a frame resolution of 3 MPix on an Orange Pi Pc 2 CPU with an Allwinner H5 Quad-Core ARM Cortex-A53 64 bit processor is 1.2-1.5 s.\",\"PeriodicalId\":428492,\"journal\":{\"name\":\"2022 24th International Conference on Digital Signal Processing and its Applications (DSPA)\",\"volume\":\"55 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2022-03-30\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2022 24th International Conference on Digital Signal Processing and its Applications (DSPA)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/dspa53304.2022.9790745\",\"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 24th International Conference on Digital Signal Processing and its Applications (DSPA)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/dspa53304.2022.9790745","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
摘要
提出了一种汽车车牌识别软件。该软件旨在集成到一个自动模块中,安装在保护区入口处的大门上。该软件的一个特点是使用计算机视觉算法,其实现需要最少的计算资源,因为视频流是由低端CPU处理的。本软件以c++语言的cause库的形式,利用计算机视觉库的标准函数OpenCV来执行。该软件包括以下几个步骤:图像预处理和二值化、车牌定位、车牌旋转和归一化、车牌内部分割、分割结果验证、文本识别。为了验证和测试软件,在门上安装了摄像机,并对接收到的视频数据进行后续处理。测试结果表明:正确识别概率为0.96,识别错误概率为- 0.004,缺失概率为- 0.035,错误识别概率为- 0.015,在采用Allwinner H5四核ARM Cortex-A53 64位处理器的Orange Pi Pc 2 CPU上,帧分辨率为3 MPix的帧处理时间为1.2-1.5 s。
Software for Car License Plates Recognition with Minimal Computing Resources
The paper proposes a software for recognizing car license plates. The software is intended for integration into an autonomous module for installation on a gate at the entrance to the protected area. A feature of the software is the use of computer vision algorithms, the implementation of which requires minimal computing resources, since the video stream is being processed by a low-end CPU. The software is executed in the form of the caused library in language C ++ with use of standard functions of library of computer vision OpenCV. The software consists of following steps: image preprocessing and binarization, license plate localization, plate rotation and normalization, segmentation inside the license plate, segmentation result validation, text recognition. To verify and test the software, a camera was installed on the gate with the subsequent processing of the received video data. As a result of testing probability of correct recognition 0.96, probability of recognition error - 0.004, probability of missing - 0.035, probability of false recognition - 0.015, the frame processing time at a frame resolution of 3 MPix on an Orange Pi Pc 2 CPU with an Allwinner H5 Quad-Core ARM Cortex-A53 64 bit processor is 1.2-1.5 s.