Lightweight Fusion Channel Attention Convolutional Neural Network for Helmet Recognition

EasyChair Preprint 8897

Abstract

Recently, in the context of complex production and construction environments, the detection of unsafe behavior becomes more and more necessary to ensure the safety of construction projects.In this paper, a multi-level pyramidal feature fusion network based on an attention mechanism is proposed for the detection and identification of helmets worn by personnel. To improve the de- tection speed and accuracy, the network uses a residual block structure design and introduces the ECAttention channel attention mechanism to achieve cross- channel interaction. By doing so, it significantly reduces the complexity of the model while maintaining a high level of performance. To verify the effective- ness of the proposed detection network, this study compares some outstanding detection methods, drawing on existing public datasets and images obtained from the Internet. The results show the proposed network’s detection effi- ciency is higher, demonstrating the ability to achieve real-time high-precision detection of helmets worn at production sites.

Keyphrases: Helmet Detection, channel attention, feature fusion, multi-scale

@booklet{EasyChair:8897,
  author    = {Chang Xu and Jinyu Tian and Zhiqiang Zeng},
  title     = {Lightweight Fusion Channel Attention Convolutional Neural Network for Helmet Recognition},
  howpublished = {EasyChair Preprint 8897},
  year      = {EasyChair, 2022}}