Journal of System Simulation
Abstract
Abstract: Aiming at the high missed detection rates and low accuracy of existing YOLO for occlusion and multi-scale pedestrian targets, an improved pedestrian detection algorithm is proposed. YOLO backbone is modified to enhance the capabilities of cross-scale feature extraction. To increase thepedestrian feature fusion capabilities of different scales, a spatial pyramid pooling module and two attention mechanisms are introduced at different positions in front of YOLO layers. Aiming at the detection performance degradation due to the extreme complexity of network module and to improve the model training efficiency, the network structure is pruned according to the actual situation. Experimental results show that compared with YOLOv3 etc, YOLO-SSC-s model can effectively improve the medium and small pedestrian targets detection accuracy and speed, and reduce the missed detection rates under the condition of occlusion.
Recommended Citation
Xiang, Nan; Wang, Lu; Jia, Chongliu; Jian, Yuemou; and Ma, Xiaoxia
(2023)
"Simulation of Occluded Pedestrian Detection Based on Improved YOLO,"
Journal of System Simulation: Vol. 35:
Iss.
2, Article 6.
DOI: 10.16182/j.issn1004731x.joss.21-0915
Available at:
https://dc-china-simulation.researchcommons.org/journal/vol35/iss2/6
First Page
286
Revised Date
2021-11-12
DOI Link
https://doi.org/10.16182/j.issn1004731x.joss.21-0915
Last Page
299
CLC
TP391
Recommended Citation
Nan Xiang, Lu Wang, Chongliu Jia, Yuemou Jian, Xiaoxia Ma. Simulation of Occluded Pedestrian Detection Based on Improved YOLO[J]. Journal of System Simulation, 2023, 35(2): 286-299.
DOI
10.16182/j.issn1004731x.joss.21-0915
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