The real-time video detection model is yet a challenging, especially in detecting construction site workers and their PPE (helmet and safety gear) and postures, since the construction site environment consists multiple complications such as different illumination levels, shadows, complex activities, a wide range of personal protective equipment (PPE) designs and colours. This paper proposes a novel computer vision (CV) system to detect the construction workers’ PPE and postures in a real-time manner. Four different recording sessions have been carried out to build a dataset of 95 videos by using a novel design of site cameras. The PPE detection included eight different types of helmets and gears and the postures detection consisted of nine classes. The Python data-labelling tool was used to annotate the selected datasets and the labelled datasets were used to build a detection model based on the TensorFlow environment. The proposed method consists of two layers of decision trees, which was tested and validated on two videos of 2000 frames. The proposed model achieves high-performance results in both identification and recall ratios over 83% and 95%, respectively. It also achieved higher accuracy in classifying the postures over 72% and 64% in model testing and validation. The proposed model can promote potential improvements in the application of real-time video analysis in actual site conditions.
Publication Date: 2019
Source URL: Link to URL
Publication Types: Books, Reports, Papers, and Research Articles
Topics: Computer Vision; Construction Personnel; Personal Protective Equipment; Video Imaging Detectors; Work Zones