Object detection of face mask recognition based on improved faster rcnn

YuanZhang Zhao; ShengLing Geng

doi:10.1117/12.2604524

5 October 2021 Object detection of face mask recognition based on improved faster rcnn

YuanZhang Zhao, ShengLing Geng

Proceedings Volume 11911, 2nd International Conference on Computer Vision, Image, and Deep Learning; 119110T (2021) https://doi.org/10.1117/12.2604524
Event: 2nd International Conference on Computer Vision, Image and Deep Learning, 2021, Liuzhou, China

Abstract

Object detection is a hot talking point in computer vision. Recently, as COVID-19 is spreading globally, the epidemic prevention and control has entered a normalization, wearing masks when entering and leaving public places and taking public transportation has now become normalized. The recognition of face mask is also of increasing concern. Then fast and accurate mask identification is essential. The Faster R-CNN is currently a more advanced object detection algorithm. It has the advantages of fast detection speed and high detection accuracy and is widely used in various fields. However, this method often fails to demonstrate its excellent performance in detecting small objects. This paper is based on the Faster R-CNN object detection algorithm and introduces FPN to solve multi-scale mask recognition and detection. The feature map of each resolution is introduced into the latter resolution feature map for element-wise summation operation. Fusing shallow layers with high resolution and deep layers with rich semantic information to improve the ability to detect small objects. The method is validated with 2000 face mask images as the dataset. Experimentally, the improved method proposed in this paper proves to be effective and better than the original algorithm.

Citation Download Citation

YuanZhang Zhao and ShengLing Geng "Object detection of face mask recognition based on improved faster rcnn", Proc. SPIE 11911, 2nd International Conference on Computer Vision, Image, and Deep Learning, 119110T (5 October 2021); https://doi.org/10.1117/12.2604524

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KEYWORDS

Facial recognition systems

Target detection

Detection and tracking algorithms

Feature extraction

Convolutional neural networks

Image segmentation

Convolution

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