遮蔽交通號誌的辨識

Occlusion Traffic Sign Recognizer

許淳詠(Chun-Yung Shu)

指導教授 ： 顏淑惠

淡江大學/工學院/資訊工程學系碩士班/碩士(2019年)

https://doi.org/10.6846/TKU.2019.00094

摘要

交通號誌辨識在智慧型駕駛中扮演著很重要的角色，能夠即時提醒，並提高行車時的行車安全。本篇論文提出基於類神經網路的學習方式，利用regional occlusion mask的方法，達到改善對於遮蔽交通號誌因遮蔽的關係而失去號誌整體特徵，導致辨識錯誤的問題，並且提高辨識時的準確度。本篇系統也對遮蔽號誌做全域性和區域性的特徵學習，遮蔽號誌透過全域性的特徵學習後，再經由對遮蔽號誌五個區域做區域性的學習，目的是希望能夠解決部分遮蔽的影像所帶來失去整體特徵的問題，我們也利用自己收集的號誌影像(以台灣的北市、新北市)區域為主、德國公開的資料集GTSRB來做實驗。

關鍵字

遮蔽號誌辨識 ； 類神經網路 ； 區域遮罩

並列摘要

Traffic sign recognition is very important in the intelligent driving. It enables to remind drivers in real time, and enhance the driving safety. In this paper, we propose a method based on the neural network learning, utilizing the regional occlusion mask method, to improve occlusion traffic sign recognition. This paper utilizes global and local to learn feature of occlusion traffic sign, after learning global feature of occlusion traffic sign, then learning for five local regions of occlusion traffic sign. It expect to solve partial occlusion image, to lose the whole feature problem. We also collect traffic sign images from Taipei City and New Taipei City of Taiwan. The proposed system is tested by our own dataset and German public dataset. The experimental results are promising.

並列關鍵字

Occlusion traffic sign recognition ； neural network ； regional mask

參考文獻

[1] Xianyan Kuang, Wenbin Fu, Liu Yang, “Real-Time Detection and Recognition of Road Traffic Signs Using MSER and Random Forests,” International Journal of Online and Biomedical Engineering (iJOE), Vol. 14, No. 3, 2018, pp. 34- 51.

[2] Mohit Singh, Manish Kumar Pandey, Lakshya Malik, “Traffic Sign Detection and Recognition for Autonomous Vehicles,” International Journal of Advance Research, Ideas and Innovations In Technology (IJARIIT), Vol. 4, No. 2, 2018, pp. 1666- 1670.

[3] Abrarul Haque Siddique, Dr. Preetam Suman & Kavita Agarwal, “Traffic Sign Detection and Recognition Using Digital Image Processing,” International

Journal of Advanced Research in Computer Science, Vol. 9, No.2, 2018, pp.204-208.

[4] Yi Yang, Hengliang Luo, Huarong Xu, and FuchaoWu, “Towards Real-Time