Research on Mask-Wearing Detection Algorithm Based on Improved YOLOv5

被引:35
作者
Guo, Shuyi [1 ]
Li, Lulu [1 ]
Guo, Tianyou [1 ]
Cao, Yunyu [1 ]
Li, Yinlei [1 ]
机构
[1] North China Univ Water Resources & Elect Power, Sch Mech Engn, 36 Beihuan Rd, Zhengzhou 450045, Peoples R China
关键词
object detection; YOLOv5; Coordinate Attention; BiFPN;
D O I
10.3390/s22134933
中图分类号
O65 [分析化学];
学科分类号
070302 ; 081704 ;
摘要
COVID-19 is highly contagious, and proper wearing of a mask can hinder the spread of the virus. However, complex factors in natural scenes, including occlusion, dense, and small-scale targets, frequently lead to target misdetection and missed detection. To address these issues, this paper proposes a YOLOv5-based mask-wearing detection algorithm, YOLOv5-CBD. Firstly, the Coordinate Attention mechanism is introduced into the feature fusion process to stress critical features and decrease the impact of redundant features after feature fusion. Then, the original feature pyramid network module in the feature fusion module was replaced with a weighted bidirectional feature pyramid network to achieve efficient bidirectional cross-scale connectivity and weighted feature fusion. Finally, we combined Distance Intersection over Union with Non-Maximum Suppression to improve the missed detection of overlapping targets. Experiments show that the average detection accuracy of the YOLOv5-CBD model is 96.7%-an improvement of 2.1% compared to the baseline model (YOLOv5).
引用
收藏
页数:16
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