Real-time dense traffic detection using lightweight backbone and improved path aggregation feature pyramid network

被引：14

作者：

Guo, Feng ^{[1
]}

Wang, Yi ^{[2
]}

Qian, Yu ^{[3
]}

机构：

[1] Shandong Univ, Sch Qilu Transportat, Jinan 250002, Shandong, Peoples R China

[2] Univ South Carolina, Dept Mech Engn, Columbia, SC 29208 USA

[3] Univ South Carolina, Dept Civil & Environm Engn, Columbia, SC 29208 USA

来源：

JOURNAL OF INDUSTRIAL INFORMATION INTEGRATION | 2023年 / 31卷

关键词：

Lightweight model; Feature fusion; Computer vision; Railroad grade crossing; Vehicle detection; CONVOLUTIONAL NEURAL-NETWORK; VEHICLE DETECTION;

D O I：

10.1016/j.jii.2022.100427

中图分类号：

TP39 [计算机的应用];

学科分类号：

081203 ; 0835 ;

摘要：

An informed traffic situation at a grade crossing is essential for traffic management. Current detection systems are expensive in computation and unsatisfactory in dense traffic instance detection. This work proposes a lightweight dense traffic detection network (DTDNet-Lite) for improved detection performance to address the issues above and benefit the development of the portable traffic monitoring system, especially at the railroad -highway grade crossing areas. An improved path aggregation feature pyramid network (iPAFPN) is developed for multiple-scale feature fusion. A lightweight backbone, ResNet18, is employed to extract features efficiently and accurately. Comprehensive experiments have been conducted on the VOC 2007 dataset and our customized grade crossing dataset. Results indicate the superiority of DTDNet-Lite, paving the way for the deployment of efficient embedded artificial intelligence (AI) computing devices for better traffic monitoring at grade crossings.

引用

页数：15