![](/images/graphics-bg.png)
Fast Traffic Sign Detection Approach Based on Lightweight Network and Multilayer Proposal Network
المؤلف
المصدر
العدد
المجلد 2020، العدد 2020 (31 ديسمبر/كانون الأول 2020)، ص ص. 1-13، 13ص.
الناشر
Hindawi Publishing Corporation
تاريخ النشر
2020-06-19
دولة النشر
مصر
عدد الصفحات
13
التخصصات الرئيسية
الملخص EN
Vision-based traffic sign detection plays a crucial role in intelligent transportation systems.
Recently, many approaches based on deep learning for traffic sign detection have been proposed and showed better performance compared with traditional approaches.
However, due to difficult conditions in driving environment and the size of traffic signs in traffic scene images, the performance of deep learning-based methods on small traffic sign detection is still limited.
In addition, the inference speed of current state-of-the-art approaches on traffic sign detection is still slow.
This paper proposes a deep learning-based approach to improve the performance of small traffic sign detection in driving environments.
First, a lightweight and efficient architecture is adopted as the base network to address the issue of the inference speed.
To enhance the performance on small traffic sign detection, a deconvolution module is adopted to generate an enhanced feature map by aggregating a lower-level feature map with a higher-level feature map.
Then, two improved region proposal networks are used to generate proposals from the highest-level feature map and the enhanced feature map.
The proposed improved region proposal network is designed for fast and accuracy proposal generation.
In the experiments, the German Traffic Sign Detection Benchmark dataset is used to evaluate the effectiveness of each enhanced module, and the Tsinghua-Tencent 100K dataset is used to compare the effectiveness of the proposed approach with other state-of-the-art approaches on traffic sign detection.
Experimental results on Tsinghua-Tencent 100K dataset show that the proposed approach achieves competitive performance compared with current state-of-the-art approaches on traffic sign detection while being faster and simpler.
نمط استشهاد جمعية علماء النفس الأمريكية (APA)
Nguyen, Hoanh. 2020. Fast Traffic Sign Detection Approach Based on Lightweight Network and Multilayer Proposal Network. Journal of Sensors،Vol. 2020, no. 2020, pp.1-13.
https://search.emarefa.net/detail/BIM-1190625
نمط استشهاد الجمعية الأمريكية للغات الحديثة (MLA)
Nguyen, Hoanh. Fast Traffic Sign Detection Approach Based on Lightweight Network and Multilayer Proposal Network. Journal of Sensors No. 2020 (2020), pp.1-13.
https://search.emarefa.net/detail/BIM-1190625
نمط استشهاد الجمعية الطبية الأمريكية (AMA)
Nguyen, Hoanh. Fast Traffic Sign Detection Approach Based on Lightweight Network and Multilayer Proposal Network. Journal of Sensors. 2020. Vol. 2020, no. 2020, pp.1-13.
https://search.emarefa.net/detail/BIM-1190625
نوع البيانات
مقالات
لغة النص
الإنجليزية
الملاحظات
Includes bibliographical references
رقم السجل
BIM-1190625
قاعدة معامل التأثير والاستشهادات المرجعية العربي "ارسيف Arcif"
أضخم قاعدة بيانات عربية للاستشهادات المرجعية للمجلات العلمية المحكمة الصادرة في العالم العربي
![](/images/ebook-kashef.png)
تقوم هذه الخدمة بالتحقق من التشابه أو الانتحال في الأبحاث والمقالات العلمية والأطروحات الجامعية والكتب والأبحاث باللغة العربية، وتحديد درجة التشابه أو أصالة الأعمال البحثية وحماية ملكيتها الفكرية. تعرف اكثر
![](/images/kashef-image.png)