Computer Engineering and Applications ›› 2021, Vol. 57 ›› Issue (22): 177-181.DOI: 10.3778/j.issn.1002-8331.2006-0354

• Pattern Recognition and Artificial Intelligence • Previous Articles     Next Articles

Lightweight and High-Precision Convolutional Neural Network for Helmet Recognition Method

CHEN Liu, CHEN Mingju, XUE Zhishuang, LUO Shisheng   

  1. School of Information Engineering, Sichuan University of Science & Engineering, Zigong, Sichuan 643000, China
  • Online:2021-11-15 Published:2021-11-16



  1. 四川轻化工大学 人工智能四川省重点实验室,四川 自贡 643000


Due to the complexity of construction environment, machine vision based helmet recognition methods often have false detections and missed detections. In order to improve the accuracy of helmet recognition in complex environments and satisfy the real-time requirement, this paper proposes a lightweight and high-precision convolutional neural network based on the characteristics of the receptive field. The convolutional neural network is based on the RFBnet network, and a feature pyramid network module is embedded to the neural network to represent the shallow semantic information and the deep semantic information simultaneously, so as to improve the accuracy of helmet recognition with different shapes and sizes in complex construction environments. SE-Ghost module is used to lighten the network backbone and reduce the complexity of the network without the network feature extraction capability changed. In the experiment, the proposed convolutional neural network based on receptive field is compared with current main convolutional neural networks, the results show that the proposed network has higher accuracy than YOLO-v3, RFBnet-300 and RFBnet-512 networks, and increases 1.60 percentage points, 3.62 percentage points and 0.98 percentage points respectively, while the detection speed reaches 20 frame/s.

Key words: helmet detection, convolutional neural network, receptive field structure, feature pyramid



关键词: 安全帽检测, 卷积神经网络, 感受野结构, 特征金字塔