Fast helmet-wearing-condition detection based on improved YOLOv2

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FANG Ming 方 明, et al. “Fast Helmet-Wearing-Condition Detection Based on Improved YOLOv2”. Optics and Precision Engineering, vol. 27, no. 5, 2019, pp. 1196-05, https://doi.org/10.3788/ope.20192705.1196.
FANG Ming 方. 明., SUN Teng-teng 孙., & SHAO Zhen 邵. 桢. (2019). Fast helmet-wearing-condition detection based on improved YOLOv2. Optics and Precision Engineering, 27(5), 1196-1205. https://doi.org/10.3788/ope.20192705.1196
FANG Ming 方明, SUN Teng-teng 孙, SHAO Zhen 邵桢. Fast helmet-wearing-condition detection based on improved YOLOv2. Optics and Precision Engineering. 2019;27(5):1196-205.
Citations
Title Journal Journal Categories Citations Publication Date
Safety Helmet Wearing Detection Model Based on Improved YOLO-M IEEE Access
  • Technology: Electrical engineering. Electronics. Nuclear engineering
  • Science: Science (General): Cybernetics: Information theory
  • Technology: Electrical engineering. Electronics. Nuclear engineering: Electric apparatus and materials. Electric circuits. Electric networks
  • Technology: Electrical engineering. Electronics. Nuclear engineering: Telecommunication
  • Technology: Electrical engineering. Electronics. Nuclear engineering: Electronics
  • Technology: Electrical engineering. Electronics. Nuclear engineering: Electronics
 2023
Citations Analysis
Category Category Repetition
Technology: Electrical engineering. Electronics. Nuclear engineering1
Science: Science (General): Cybernetics: Information theory1
Technology: Electrical engineering. Electronics. Nuclear engineering: Electric apparatus and materials. Electric circuits. Electric networks1
Technology: Electrical engineering. Electronics. Nuclear engineering: Telecommunication1
Technology: Electrical engineering. Electronics. Nuclear engineering: Electronics1
The category Technology: Electrical engineering. Electronics. Nuclear engineering 1 is the most commonly referenced area in studies that cite this article.