基于单目三维尺度恢复的人机碰撞识别与预警OA北大核心CSTPCD

Efficient construction safety managements facilitate healthy and high-quality development of the construction industry.To ensure safe construction,it is crucial to prevent human-machine collision accidents.To accurately recognize the safety risks of human-machine operation,an automated method for human-machine collision risk recognition and warning was proposed based on self-calibration of dual-scale monocular cameras.This method recovered the three-dimensional scale of monocular vision based on the geometric characteristics analysis of the construction site and the extraction of target features,leading to precise measurement of the spatial distance between humans and machines.Furthermore,an approach for human-machine collision warning and visual simulation was proposed based on the kinematic characteristics of construction machinery.This method can trigger multi-level collision warnings based on a human-machine distance threshold.A construction project in Shanghai was selected as a test case and achieved accurate object detections(with average accuracy of 91.2％),spatial distance measurements(with accuracy above 98％)and collision event assessments,with the algorithm frame rate meeting real-time monitoring requirements.