Transfer learning and deep convolutional neural networks for safety guardrail detection in 2D images

摘要

Safety has been a concern for the construction industry for decades. Unsafe conditions and behaviors are considered as the major causes of construction accidents. The current safety inspection of conditions and behaviors heavily rely on human efforts which are limited onsite. To improve the safety performance of the industry, a more efficient approach to identify the unsafe conditions on site is required to supplement the current manual inspection practice. A promising way to supplement the current manual safety inspection is automated and intelligent monitoring/inspection through information and sensing technologies, including localization techniques, environment monitoring, image processing and etc. To assess the potential benefits of contemporary technologies for onsite safety inspection, the authors focused on real-time guardrail detection, as unprotected edges are the ones cause for workers falling from heights.