Electronic Science and Technology ›› 2023, Vol. 36 ›› Issue (4): 44-51.doi: 10.16180/j.cnki.issn1007-7820.2023.04.006

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Helmet Wearing Detection Based on Enhanced Feature Fusion Network

CUI Zhuodong,CHEN Wei,YIN Zhong   

  1. School of Optical-Electrical and Computer Engineering,University of Shanghai for Science and Technology,Shanghai 200093,China
  • Received:2021-10-22 Online:2023-04-15 Published:2023-04-21
  • Supported by:
    National Natural Science Foundation of China(61703277)

Abstract:

Wearing safety helmet is one of the important ways to ensure the safety of workers in production activities. The detection accuracy and speed of the existing helmet detector need to be improved, which makes it difficult for the existing detectors to be applied in real production activities on a large scale. To solve these problems, the helmet detector based on EfficientDet is introduced and improved from the perspective of feature fusion. Specifically, the model reduces the information loss in the process of feature fusion using feature supplement module, and improves the efficiency of feature fusion using improved feature pyramid and adaptive spatial fusion module, and finally achieves the goal of improving performance. Experimental results show that the accuracy of the improved model on the helmet wearing data set is 83.03%, and the model size does not increase significantly. The accuracy of the model on PASCAL VOC 2007 data set is 82.76%.

Key words: helmet wearing detection, feature fusion, feature pyramid, object detection, EfficientDet, spatial fusion, deep learning, convolutional neural network

CLC Number: 

  • TP391