A Benchmark for Detection and Recognition of Bangladeshi Traffic Signs in Real-world Images

Khan, Rizwanul Haque; Shanto, Md Saimul Haque; Ashik, Ahmed Nusayer

dc.contributor.author	Khan, Rizwanul Haque
dc.contributor.author	Shanto, Md Saimul Haque
dc.contributor.author	Ashik, Ahmed Nusayer
dc.date.accessioned	2023-03-15T08:26:50Z
dc.date.available	2023-03-15T08:26:50Z
dc.date.issued	2022-05-30
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dc.identifier.uri	http://hdl.handle.net/123456789/1769
dc.description	Supervised by Dr. Md. Hasanul Kabir, Professor, Co-supervisor Mr. Sabbir Ahmed Lecturer Department of Computer Science and Engineering(CSE), Islamic University of Technology (IUT) Board Bazar, Gazipur-1704, Bangladesh. This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2022.	en_US
dc.description.abstract	Traffic sign detection is an indispensable part of autonomous driving and transportation safety systems. However, the accurate detection and recognition of traffic signs remain challenging, especially under extreme conditions, such as various weather and geo-social features. Though a lot of work has been done in the domain of Traffic Sign Detection and Recognition (TSDR) systems, only a few of them focus on a dataset that comprises the real-world challenges. Moreover, in the context of Bangladesh, there is no well-defined public dataset, let alone one that focuses on real-life challenges. The geo-social features of Bangladesh add some unique challenges that are not seen in most parts of the world. This proposal aims to address the lack of quality Bangla traffic sign detection dataset. To accomplish this task, traffic sign images will be extracted from videos collected from Bangladeshi roads. A performance benchmark will be presented by applying state-of-the-art methods to the said dataset. Using the best-performing method, an autonomous driver notification system will be developed to alert the drivers on the go.	en_US
dc.language.iso	en	en_US
dc.publisher	Department of Computer Science and Engineering(CSE), Islamic University of Technology(IUT), Board Bazar, Gazipur, Bangladesh	en_US
dc.subject	Object detection, CNN, BdTSDB, YOLO, Fast R-CNN, Faster R-CNN, EfficientDet, ResNet	en_US
dc.title	A Benchmark for Detection and Recognition of Bangladeshi Traffic Signs in Real-world Images	en_US
dc.type	Thesis	en_US