文献类型：期刊文献

英文题名：Pedestrian detection based on YOLOv3 multimodal data fusion

作者：Wang, Cheng[1];Liu, Yuan-sheng[2];Chang, Fei-xiang[1];Lu, Ming[3]

通讯作者：Liu, YS[1]

机构：[1]Beijing Union Univ, Beijing Key Lab Informat Serv Engn, Beijing, Peoples R China;[2]Beijing Union Univ, Coll Robot, Beijing, Peoples R China;[3]Beijing Union Univ, Coll Appl Sci & Technol, Beijing, Peoples R China

第一机构：北京联合大学北京市信息服务工程重点实验室

通讯机构：[1]corresponding author), Beijing Union Univ, Coll Robot, Beijing, Peoples R China.|[1141739]北京联合大学机器人学院;[11417]北京联合大学;

年份：2022

卷号：10

期号：1

起止页码：832-845

外文期刊名：SYSTEMS SCIENCE & CONTROL ENGINEERING

收录：EI(收录号：20224413045563);Scopus(收录号：2-s2.0-85140910519);WOS:【ESCI(收录号:WOS:000867421200001)】；

基金：This work was supported by the National Key Research and Development Program of China: [Grant Number 2021YFC3001300]; the National Natural Science Foundation of China: [Grant Number 61931012]; Beijing Natural Science Foundation: [Grant Number 4222025]; the Academic Research Projects of Beijing Union University aA.Grant Number ZK10202208aAI.

语种：英文

外文关键词：Pedestrian detection; Improved YOLOv3; Data fusion; Embedded devices

摘要：Multi-sensor fusion has essential applications in the field of target detection. Considering the current actual demand for miniaturization of on-board computers for driverless vehicles, this paper uses the multimodal data YOLOv3 (MDY) algorithm for pedestrian detection on embedded devices. The MDY algorithm uses YOLOv3 as the basic framework to improve pedestrian detection accuracy by optimizing anchor frames and adding small target detection branches. Then the algorithm is accelerated by using TensorRT technology to improve the real-time performance in embedded devices. Finally, a hybrid fusion framework is used to fuse the LIDAR point cloud data with the improved YOLOv3 algorithm to compensate for the shortcomings of a single sensor and improve the detection accuracy while ensuring speed. The improved YOLOv3 improves AP by 6.4% and speed by 11.3 FPS over the original algorithm. The MDY algorithm achieves better performance on the KITTI dataset. To further verify the feasibility of the MDY algorithm, an actual testwas conducted on an unmanned vehicle with Jetson TX2 embedded device as the on-board computer within the campus scenario, and the results showed that the MDY algorithm achieves 90.8% accuracy under real-time operation and can achieve adequate detection accuracy and real-time performance on the embedded device.