Intelligent early warning method for construction machinery hazardous area intrusion based on deep learning and depth estimation
To solve the problem of engineering safety accidents caused by workers and construction machinery intruding into construction hazardous areas,etc.,a multi-task-driven dynamic identification and early warning method for hazardous area intrusion events is proposed.A Yolov8 network with permutation variable convolu-tional DConv2 module was used for target class detection and coordinate outer contour extraction to improve the recognition accuracy of mobile construction machinery.It was also combined with the Monodepth2 monocular depth estimation network for depth information estimation and coordinate unification to calculate the true state of a worker or construction machine at a distance from a hazardous area event,which is used to assess the risk of hazardous area intrusion.The model performance was compared with Yolov8,the original Yolov8 and Yolov5 models with different modification layers and four scenarios were designed for model performance validation.The study shows that the model improves 2.99%and 3.55%in construction machinery identification accuracy and contour extraction accuracy respectively,and can maintain an accuracy rate of over 94%in the identification of workers and construction machinery intrusion into mobile construction machinery hazardous area,with an FPS of around 17.7,which can effectively achieve intelligent dynamic warning of construction hazardous area intrusion.
construction safetyhazardous areaintrusion event early warninghuman-machine collisiondeep learningdepth estimation
国家科技期刊平台
NSTL
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