首页|Studies from Heriot-Watt University Further Understanding of Machine Learning (Comparison of Machine Learning Approaches for Robust and Timely Detection of PPE in Construction Sites)
Studies from Heriot-Watt University Further Understanding of Machine Learning (Comparison of Machine Learning Approaches for Robust and Timely Detection of PPE in Construction Sites)
扫码查看
点击上方二维码区域，可以放大扫码查看
原文链接
NETL
NSTL
By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News-Investigators publish new report on artificial intelligence. According to news originating from Edinburgh, United Kingdom, by NewsRx correspondents, research stated, "Globally, workplace safety is a critical concern, and statistics highlight the widespread impact of occupational hazards. According to the International Labour Organization (ILO), an estimated 2.78 million work-related fatalities occur worldwide each year, with an additional 374 million non-fatal workplace injuries and illnesses." The news correspondents obtained a quote from the research from Heriot-Watt University: "These incidents result in significant economic and social costs, emphasizing the urgent need for effective safety measures across industries. The construction sector in particular faces substantial challenges, contributing a notable share to these statistics due to the nature of its operations. As technology, including machine vision algorithms and robotics, continues to advance, there is a growing opportunity to enhance global workplace safety standards and mitigate the human toll of occupational hazards on a broader scale. This paper explores the development and evaluation of two distinct algorithms designed for the accurate detection of safety equipment on construction sites. The first algorithm leverages the Faster R-CNN architecture, employing ResNet-50 as its backbone for robust object detection. Subsequently, the results obtained from Faster R-CNN are compared with those of the second algorithm, Few-Shot Object Detection (FsDet)."
NETL
NSTL
