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Deep learning might seem like a complex, abstract concept, but it has helped Kapernikov to solve very concrete challenges. The Kapernikov team recently developed a Proof of Concept (PoC), based on deep learning techniques, that enables to see whether people wear their ,safety helmets, at …
powerful object ,detection, and classiﬁcation capacity (e.g., Rubaiyatetal.,Shresthaetal.,Waranusastetal., Doungmala et al. , Jia et al. , and Li et al. ). Remarkable studies are made by Rubaiyat et al. , who proposed an automatic ,detection, method to obtain the features of construction workers and ,safety helmets, and
C. ,Safety helmets detection, The ﬁnal step is ,safety helmet detection,. After the pedes-trian classiﬁcation, the head region need to be determined. Meanwhile, the color space transformation and color feature discrimination are implemented to ascertain whether wearing ,safety helmets, or not for perambulatory workers. The image
1/2/2018, · Surveillance is essential for the ,safety, of power substation. The ,detection, of whether wearing ,safety helmets, or not for perambulatory workers is the key component of overall intelligent surveillance system in power substation. In this paper, a novel and practical ,safety helmet detection, framework based on computer vision, machine learning and image processing is proposed. In order to ...
Abstract: Although motorcycle ,safety helmets, are known for preventing head injuries, in many countries, the use of motorcycle ,helmets, is low due to the lack of police power to enforcing ,helmet, laws. This paper presents a system which automatically detect motorcycle riders and determine that they are wearing ,safety helmets, or not. The system extracts moving objects and classifies them as a ...
The ,detection, of whether wearing ,safety helmets, or not for perambulatory workers is the key component of overall intelligent surveillance system in power substation. In this paper, a novel and practical ,safety helmet detection, framework based on computer vision, machine learning and image processing is proposed.
Shrestha et al. developed a framework for construction ,safety, visualization and developed and tested an edge ,detection, algorithm. The framework included the installation of a set of CCTV cameras, a powerful server at the site office, and equipment to send warning messages to ,safety,-related personnel once the program detected workers not wearing hard hats in the construction sites.