“第二十一章 车牌识别”版本间的差异
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*[http://aigraph.cslt.org/courses/21/course-21.pptx 课件] | *[http://aigraph.cslt.org/courses/21/course-21.pptx 课件] | ||
*小清爱提问：机器如何识别车牌[https://mp.weixin.qq.com/s?__biz=Mzk0NjIzMzI2MQ==&mid=2247485046&idx=1&sn=4ca0402b4bc576d140c5d03d79879431&chksm=c3080cb4f47f85a2d5e73c1b4039f44f288a7c66d03d2be203dd1c33516dc7795bf286e2faaa&scene=178#rd] | *小清爱提问：机器如何识别车牌[https://mp.weixin.qq.com/s?__biz=Mzk0NjIzMzI2MQ==&mid=2247485046&idx=1&sn=4ca0402b4bc576d140c5d03d79879431&chksm=c3080cb4f47f85a2d5e73c1b4039f44f288a7c66d03d2be203dd1c33516dc7795bf286e2faaa&scene=178#rd] | ||
| − | * | + | *小清爱提问：目标检测中的YOLO网络长什么样？[https://mp.weixin.qq.com/s?__biz=Mzk0NjIzMzI2MQ==&mid=2247489867&idx=1&sn=0daa55f14d6ccd8e201f3de4533a791f&chksm=c3081389f47f9a9fa4fcc90aaba0432cfb8aed471ef95574dc856c3619f699cc0f578c58e45a&scene=178&cur_album_id=3052762821081645063#rd] |
==扩展阅读== | ==扩展阅读== | ||
| − | * AI100问：什么是YOLO模型 | + | * AI100问：什么是YOLO模型 [http://aigraph.cslt.org/ai100/AI100-125-什么是YOLO网络.pdf] |
| − | * AI100问：机器如何识别车牌 | + | * AI100问：机器如何识别车牌[http://aigraph.cslt.org/ai100/AI-100-100-浅谈车牌识别.pdf] |
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==视频展示== | ==视频展示== | ||
| − | * YOLO visulaization [http://aigraph.cslt.org/courses/21/ | + | * YOLO visulaization [http://aigraph.cslt.org/courses/21/yolo2-plate.mp4] |
* YOLO-v3 [http://aigraph.cslt.org/courses/21/YOLOv3-show.mp4] | * YOLO-v3 [http://aigraph.cslt.org/courses/21/YOLOv3-show.mp4] | ||
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==演示链接== | ==演示链接== | ||
| + | * 旷世科技在线演示[https://www.faceplusplus.com.cn/license-plate-recognition/] | ||
| + | * 云脉展示[https://www.yunmaiocr.com/goPlate#] | ||
| + | * 薪火科技[https://www.xinhuokj.com/ocr.html] | ||
* DTK车牌识别在线演示[https://www.dtksoft.com/lprdemo] | * DTK车牌识别在线演示[https://www.dtksoft.com/lprdemo] | ||
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==开发者资源== | ==开发者资源== | ||
| + | * OpenCV 车牌识别流程和样例程序[https://codeantenna.com/a/XVQaHQ0m6T][https://github.com/longzix/SVM-/tree/master] | ||
* 基于Yolo v4的车牌识别 [https://learnopencv.com/automatic-license-plate-recognition-using-deep-learning/] | * 基于Yolo v4的车牌识别 [https://learnopencv.com/automatic-license-plate-recognition-using-deep-learning/] | ||
* ALPR in Unscontrained Scenarios [https://github.com/sergiomsilva/alpr-unconstrained] | * ALPR in Unscontrained Scenarios [https://github.com/sergiomsilva/alpr-unconstrained] | ||
2023年8月13日 (日) 01:54的最后版本
教学资料
扩展阅读
视频展示
演示链接
开发者资源
高级读者
- Arafat M Y, Khairuddin A S M, Khairuddin U, et al. Systematic review on vehicular licence plate recognition framework in intelligent transport systems[J]. IET Intelligent Transport Systems, 2019, 13(5): 745-755. [15]
- Srikanth P, Kumar A. Automatic vehicle number plate detection and recognition systems: Survey and implementation[M]//Autonomous and Connected Heavy Vehicle Technology. Academic Press, 2022: 125-139. [16]
- Zherzdev S, Gruzdev A. Lprnet: License plate recognition via deep neural networks[J]. arXiv preprint arXiv:1806.10447, 2018. [17]
- Xie L, Ahmad T, Jin L, et al. A new CNN-based method for multi-directional car license plate detection[J]. IEEE Transactions on Intelligent Transportation Systems, 2018, 19(2): 507-517. [18]
- J. Redmon, S. Divvala, R. Girshick, A. Farhadi You only look once: unified, real-time object detection Proc. IEEE Conf. Comput. Vis. Pattern Recognit. (2016), pp. 779-788 [19]
