Real-time Pedestrian Detection Algorithm Based on Improved YOLOv3

As a research hotspot in the field of current computer vision, pedestrian detection is widely applied to many fields, such as video surveillance and autonomous driving. However, the accuracy of pedestrian detection under video surveillance is poor, and the miss rate of small target pedestrians is hi...

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Bibliographic Details
Published in:Journal of physics. Conference series 2021-08, Vol.2002 (1), p.12075
Main Authors: Xi, Xianchang, Huang, Zhikai, Ning, Lingyi, Zhang, Yang
Format: Article
Language:English
Subjects:
Accuracy
Algorithms
Computer vision
Multi-scale detection
Pedestrian detection
Pedestrians
SPP
Surveillance
Target detection
YOLOv3
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Summary:As a research hotspot in the field of current computer vision, pedestrian detection is widely applied to many fields, such as video surveillance and autonomous driving. However, the accuracy of pedestrian detection under video surveillance is poor, and the miss rate of small target pedestrians is high. In this paper, an improves the YOLOv3 algorithm and a YOLOv3-Multi pedestrian detection model had been proposed. First, referring to the residual structure of DarkNet, the shallow features and deep features had been up-sampled and connected to obtain a multi-scale detection layer. Then, according to different special detection categories, the spatial pyramid pool (SPP) is introduced to strengthen the detection of small targets. The experimental results show that our method improves the average accuracy by 2.54%, 6.43% and 8.99%compared with YOLOv3, SSD and YOLOv2 on the VOC dataset.
ISSN:1742-6588
1742-6596
DOI:10.1088/1742-6596/2002/1/012075