Academic Journal of Computing & Information Science, 2021, 4(5); doi: 10.25236/AJCIS.2021.040511.
Ruolan Deng1, Biqiang Guan2, Ziteng Li3, Jiahao Wang4
1University of Leeds, Leeds, United Kingdom
2Beijing University of Chemical Technology, Beijing, China
3Beijing Information Science and Technology University, Beijing, China
4Zhejiang University, Hangzhou, Zhejiang, China
These authors contributed equally to this work
This paper proposes a goal detection network of end-to-end multi-scale feature fusion because of the tiny pedestrian target and blocking in pedestrian detection. This algorithm is based on the YOLOv3 network, fully integrates multi-scale features, enhances the expression ability of small target features, improves the robustness of pedestrian detection in complex environments, and improves pedestrian detection accuracy based on guaranteeing real-time detection. In the experiment, the current mainstream pedestrian detection algorithm is compared. This algorithm effectively improves the detection accuracy in INRIA and KITTI data sets, and the average accuracy of Yolov3 in two different data sets is improved by 6% and 24.7%, respectively.
Driverless, Pedestrian Detection, Target Detection, YOLOv3
Ruolan Deng, Biqiang Guan, Ziteng Li, Jiahao Wang. Pedestrian detection based on multi-layer feature fusion. Academic Journal of Computing & Information Science (2021), Vol. 4, Issue 5: 76-84. https://doi.org/10.25236/AJCIS.2021.040511.
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