Fast Vehicle and Pedestrian Detection Using Improved Mask R-CNN
المؤلفون المشاركون
Xu, Lin
Xu, Chenchen
Wang, Guili
Yan, Songsong
Yu, Jianghua
Zhang, Baojun
Dai, Shu
Li, Yu
المصدر
Mathematical Problems in Engineering
العدد
المجلد 2020، العدد 2020 (31 ديسمبر/كانون الأول 2020)، ص ص. 1-15، 15ص.
الناشر
Hindawi Publishing Corporation
تاريخ النشر
2020-05-31
دولة النشر
مصر
عدد الصفحات
15
التخصصات الرئيسية
الملخص EN
This study presents a simple and effective Mask R-CNN algorithm for more rapid detection of vehicles and pedestrians.
The method is of practical value for anticollision warning systems in intelligent driving.
Deep neural networks with more layers have greater capacity but also have to perform more complicated calculations.
To overcome this disadvantage, this study adopts a Resnet-86 network as a backbone that differs from the backbone structure of Resnet-101 in the Mask R-CNN algorithm within practical conditions.
The results show that the Resnet-86 network can reduce the operation time and greatly improve accuracy.
The detected vehicles and pedestrians are also screened out based on the Microsoft COCO dataset.
The new dataset is formed by screening and supplementing COCO dataset, which makes the training of the algorithm more efficient.
Perhaps, the most important part of our research is that we propose a new algorithm, Side Fusion FPN.
The parameters in the algorithm have not increased, the amount of calculation has increased by less than 0.000001, and the mean average precision (mAP) has increased by 2.00 points.
The results show that, compared with the algorithm of Mask R-CNN, our algorithm decreased the weight memory size by 9.43%, improved the training speed by 26.98%, improved the testing speed by 7.94%, decreased the value of loss by 0.26, and increased the value of mAP by 17.53 points.
نمط استشهاد جمعية علماء النفس الأمريكية (APA)
Xu, Chenchen& Wang, Guili& Yan, Songsong& Yu, Jianghua& Zhang, Baojun& Dai, Shu…[et al.]. 2020. Fast Vehicle and Pedestrian Detection Using Improved Mask R-CNN. Mathematical Problems in Engineering،Vol. 2020, no. 2020, pp.1-15.
https://search.emarefa.net/detail/BIM-1196229
نمط استشهاد الجمعية الأمريكية للغات الحديثة (MLA)
Xu, Chenchen…[et al.]. Fast Vehicle and Pedestrian Detection Using Improved Mask R-CNN. Mathematical Problems in Engineering No. 2020 (2020), pp.1-15.
https://search.emarefa.net/detail/BIM-1196229
نمط استشهاد الجمعية الطبية الأمريكية (AMA)
Xu, Chenchen& Wang, Guili& Yan, Songsong& Yu, Jianghua& Zhang, Baojun& Dai, Shu…[et al.]. Fast Vehicle and Pedestrian Detection Using Improved Mask R-CNN. Mathematical Problems in Engineering. 2020. Vol. 2020, no. 2020, pp.1-15.
https://search.emarefa.net/detail/BIM-1196229
نوع البيانات
مقالات
لغة النص
الإنجليزية
الملاحظات
Includes bibliographical references
رقم السجل
BIM-1196229
قاعدة معامل التأثير والاستشهادات المرجعية العربي "ارسيف Arcif"
أضخم قاعدة بيانات عربية للاستشهادات المرجعية للمجلات العلمية المحكمة الصادرة في العالم العربي
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